title: “DA5020.P1.Tapiawala” team: Samir Tapiawala, Omar Waid output: html_document


library(dplyr)
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library(tidyr)
library(lubridate)
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## Attaching package: 'lubridate'
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library(XML)
library(RCurl)
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## Attaching package: 'RCurl'
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library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x lubridate::as.difftime() masks base::as.difftime()
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## x dplyr::filter()          masks stats::filter()
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## x dplyr::lag()             masks stats::lag()
## x lubridate::setdiff()     masks base::setdiff()
## x lubridate::union()       masks base::union()
library(tibble)
library(countrycode)

Question 1)

Download the COVID-19 case distribution (in XML formal): https://opendata.ecdc.europa.eu/ covid19/casedistribution/xml/. Load the XML file into a browser or text editing tool and inspect it. Explore the data set as you see fit and get a sense of the data. Note that you are working with a live dataset that is updated daily. Therefore it is best to ensure that your results are data driven

Answer 1)

SAMPLE from xml file:

08/06/2020 8 6 2020 791 30 Afghanistan AF AFG 37172386 Asia

-> Components:

  1. Date: day/month/year
  2. Case: no. of new cases/per day
  3. Deaths: no. of deaths /per day
  4. Countries and Territories: country full name
  5. geoID: 2 digit ISO Code
  6. Country Territory Code: 3 digit ISO Code
  7. Population Data: 2018 census data
  8. Continent: cotinent name

-> How many DBs are used in this? 1) COVID 19: new cases and deaths 2) Population 3) World country DB: ?ISO to divide world into continent followed by country territory, country name and geoID?

Question 2)

Load the data into R (directly from the URL) and create two linked tibbles: one for country and the other for covid_19_data that contains each country’s reported case. The country tibble should contain the following: countriesAndTerritories, countryterritoryCode (primary key), popData2018, continentExp. The covid_19_data tibble should contain: id (auto incremented value that will serve as the primary key), countryterritoryCode(foreign key), dateRep, cases, deaths. You can link/join both tibbles by using the countryterritoryCode

url_covid <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/xml/" # converting url into a vector 
temp_covid <- getURL(url_covid) # creating a temporary file for the url
covid_parse <- xmlParse(temp_covid) # parsing the data from the temporary file

#view(covid_parse)
covid_xml <- xmlRoot(covid_parse) # to access top-level XMLNode in the file

xmlName(covid_xml) # to get the name of the top-level XMLNode
## [1] "records"
covid_entire <- xmlToDataFrame(covid_xml)

view(covid_entire)

glimpse(covid_entire)
## Rows: 23,428
## Columns: 11
## $ dateRep                 <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/202…
## $ day                     <fct> 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1…
## $ month                   <fct> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, …
## $ year                    <fct> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 202…
## $ cases                   <fct> 556, 656, 747, 684, 542, 575, 791, 582, 915, …
## $ deaths                  <fct> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8…
## $ countriesAndTerritories <fct> Afghanistan, Afghanistan, Afghanistan, Afghan…
## $ geoId                   <fct> AF, AF, AF, AF, AF, AF, AF, AF, AF, AF, AF, A…
## $ countryterritoryCode    <fct> AFG, AFG, AFG, AFG, AFG, AFG, AFG, AFG, AFG, …
## $ popData2018             <fct> 37172386, 37172386, 37172386, 37172386, 37172…
## $ continentExp            <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…
# All variables are represented as 'Factor" type. In future, we might have to format these variables into approproate types to run the codes and analysis
covid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})
## countriesAndTerritories                   geoId    countryterritoryCode 
##                     210                     210                     207 
##             popData2018 
##                     206
# We observed four countries/territories codes are missing. We need to add those codes in the database

# Anguilla -> "AIA"
# Bonaire, Saint Eustatius and Saba -> "BES"
# Falkland_Islands_(Malvinas) -> "FLK"
# Western_Sahara -> "ESH"


# In order to do so, we will have to change variable type for both Countryname and countrycode from 'Factor' to 'Character' ... using 'Factor', it's giving error

covid_entire$countriesAndTerritories <- as.character(covid_entire$countriesAndTerritories)
covid_entire$countryterritoryCode <- as.character(covid_entire$countryterritoryCode)


for (i in 1:nrow(covid_entire)){ # This will help to iterate through entire dataframe columns
  if(covid_entire[i,"countryterritoryCode"]==''){ # if country code value is blank/missing
    covid_entire[i,"countryterritoryCode"] <- 
      countrycode(covid_entire[i,"countriesAndTerritories"], origin ='country.name' , destination = "iso3c")
         # replace/fill the missing values from 'countrycode' package
    
  #print(covid_view[i,"countriesAndTerritories"])
  }
}

view(covid_entire)
# Rechecking, whether countrycodes numbers are matching or no:

covid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})
## countriesAndTerritories                   geoId    countryterritoryCode 
##                     210                     210                     210 
##             popData2018 
##                     206
# Population data of same countries missing:

# Anguilla -> 14731
# Bonaire, Saint Eustatius and Saba -> 19549
# Falkland_Islands_(Malvinas) -> 3234
# Western_Sahara -> 567402
# Eritrea -> 6050000

 
# Before doing so, we have to convert variable type of 'popData2018'. Mentioned as 'Factor'... to convert into 'numeric'

covid_entire$popData2018 <- as.numeric(as.character(covid_entire$popData2018))
  
  
for (p in 1:nrow(covid_entire)){
  if(covid_entire[p,"countriesAndTerritories"] == "Anguilla"){
    covid_entire[p,"popData2018"] <- 14731
  }
  if(covid_entire[p,"countriesAndTerritories"] == "Bonaire, Saint Eustatius and Saba"){
    covid_entire[p,"popData2018"] <- 19549
  }
  if(covid_entire[p,"countriesAndTerritories"] == "Falkland_Islands_(Malvinas)"){
    covid_entire[p,"popData2018"] <- 3234
  }
  if(covid_entire[p,"countriesAndTerritories"] == "Western_Sahara"){
    covid_entire[p,"popData2018"] <- 567402
  }
  if(covid_entire[p,"countriesAndTerritories"] == "Eritrea"){
    covid_entire[p,"popData2018"] <- 6050000
  }  
} 
   
view(covid_entire)          
# Rechecking, whether Population data numbers are matching or no:

covid_entire %>% select(7:10) %>% sapply(function(x){length(unique(x))})
## countriesAndTerritories                   geoId    countryterritoryCode 
##                     210                     210                     210 
##             popData2018 
##                     210
# In the dataset, Country Name, geoID and Country Code (3 digits) are reduntant. Removing geoID column/variable from the dataset. 

# Keeping Country Name: required to identify countries based on codes. 

# Also, keeping Country Code - required as 'Primary Key' in future analysis.


covid_entire = subset(covid_entire, select = -c(geoId))

glimpse(covid_entire)
## Rows: 23,428
## Columns: 10
## $ dateRep                 <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/202…
## $ day                     <fct> 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1…
## $ month                   <fct> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, …
## $ year                    <fct> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 202…
## $ cases                   <fct> 556, 656, 747, 684, 542, 575, 791, 582, 915, …
## $ deaths                  <fct> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8…
## $ countriesAndTerritories <chr> "Afghanistan", "Afghanistan", "Afghanistan", …
## $ countryterritoryCode    <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AF…
## $ popData2018             <dbl> 37172386, 37172386, 37172386, 37172386, 37172…
## $ continentExp            <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…
# We found some negative values under 'cases' and 'deaths' columns. Seems it is typo error. since we can not verify the data, it is advisable to delete those observations to avoid any misinterpretation of the data.

# Before doing so, we have to convert variable types of 'cases' and 'deaths'. Mentioned as 'Factor'... to convert into 'numeric'

covid_entire$cases <-  as.integer(as.character(covid_entire$cases))
covid_entire$deaths <-  as.integer(as.character(covid_entire$deaths))

# (1) covid_entire1 <- covid_entire[covid_entire$cases >= 0,] # creating subset to include values >= 0, thus removing all negative values with typo error

# (2) covid_entire <- covid_entire1[covid_entire1$deaths >= 0,]

#covid_entire
#view(covid_entire)
# By running above codes: (1) and (2) removed around 15 negative values, but at the same time, the process removed entire rows from the dataset. Realised that, by removing negative values of cases, positive values of deaths were getting removed from the dataset. So, decided to replace negative values by ZERO rather than losing important information from other variables.


for (n in 1:nrow(covid_entire)){ # This will help to iterate through entire dataframe columns
  if(covid_entire[n,"cases"]< 0){
    covid_entire[n,"cases"] <- 0
  } 
  if(covid_entire[n,"deaths"]< 0){
    covid_entire[n,"deaths"] <- 0
  }  
}
 
view(covid_entire)  

========================== x ======================================

Answer 2)

…To create two tibbles:

  1. Country: country
    1. name (countries and territories),
    2. country territory (3 digit code)- Primary Key,
    3. Population data 2018,
    4. continentExp
# 'country' tibble:

country <- tibble(
  "countryName" = covid_entire$countriesAndTerritories,
  "countryCode" = covid_entire$countryterritoryCode,
  "population" = covid_entire$popData2018,
  "region" = covid_entire$continentExp
) %>% distinct()

#country <- select(countryName, countryCode, population, region)

view(country)
glimpse(country)
## Rows: 210
## Columns: 4
## $ countryName <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola",…
## $ countryCode <chr> "AFG", "ALB", "DZA", "AND", "AGO", "AIA", "ATG", "ARG", "…
## $ population  <dbl> 37172386, 2866376, 42228429, 77006, 30809762, 14731, 9628…
## $ region      <fct> Asia, Europe, Africa, Europe, Africa, America, America, A…
  1. COVID 19: covid_19_data
    1. id (autoincremented value) - Primary Key
    2. country territory code - Foreign key
    3. dateRep
    4. cases
    5. deaths
# creating tibble using select() function

covid_19_data <- tibble(covid_entire %>%
    select( 
  "reportDate" = dateRep,
  "countryCode" = countryterritoryCode,
  "newCases" = cases,
  "deaths" = deaths) %>%
    mutate(dataKey = row_number()) # this code will generate rowID - autoincremental value
)

view(covid_19_data)

glimpse(covid_19_data)
## Rows: 23,428
## Columns: 5
## $ reportDate  <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/2020, 10/06/202…
## $ countryCode <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "…
## $ newCases    <dbl> 556, 656, 747, 684, 542, 575, 791, 582, 915, 787, 758, 75…
## $ deaths      <dbl> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8, 8, 3, 11, …
## $ dataKey     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
# To link/join both tibbles by using the countryterritoryCode... using countryCode, which is Prmary key in country tibble while foreign key in covid_19_data tibble 

covid_update <- tibble(
  right_join(covid_19_data, country, by = "countryCode")
)

view(covid_update)

glimpse(covid_update)
## Rows: 23,428
## Columns: 8
## $ reportDate  <fct> 14/06/2020, 13/06/2020, 12/06/2020, 11/06/2020, 10/06/202…
## $ countryCode <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "…
## $ newCases    <dbl> 556, 656, 747, 684, 542, 575, 791, 582, 915, 787, 758, 75…
## $ deaths      <dbl> 5, 20, 21, 21, 15, 12, 30, 18, 9, 6, 24, 5, 8, 8, 3, 11, …
## $ dataKey     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
## $ countryName <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan…
## $ population  <dbl> 37172386, 37172386, 37172386, 37172386, 37172386, 3717238…
## $ region      <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asi…

========================== x ======================================

Question 3)

Create a function called worldwideCases() that displays: a) the total cases worldwide, b) the number of new cases within the past day (grouped by continent)

Answer 3)

# total new cases: addition of all observations in the variable newCases in last 24 hours(per day)
# using sum() argument
# Dates of Reporting (dateRep) are as 'Factor' in the dataset, we need to convert them into 'date' category
# group_by(continent): newcases in last 24 hours
# asked for two types of output: 'total cases worldwide' and 'continent wise' 


worldwideCases <- function(dt) {
  date_parameter <- covid_entire[format(as.Date(covid_entire$dateRep, '%d/%m/%Y'), '%m/%d/%Y') == dt,]
    # creating this variable to match the date and use for output related to 'continents cases'
  date_parameter2 <- covid_entire[format(as.Date(covid_entire$dateRep, '%d/%m/%Y'), '%m/%d/%Y') <= dt,]
    # creating this variable to match the date and use for output related to 'world cases'
  casesWorld <- sum(date_parameter2$cases)   # using sum() function to calculate total cases worldwide
  casesContinent <- as.data.frame(date_parameter %>% group_by(continentExp) %>% summarise(cases = sum(cases))) # creating data frame to represent all continenets total in tabular format 
    world_wide_cases = paste("Total cases worldwide till", dt, ":", casesWorld) 
      # formating output message for 'world wide cases till the date searched for'
    continent_wide_cases = paste("Number of cases in last 24 hours:", casesContinent$continentExp, ":", casesContinent$cases)
      # formating output message for 'continent wise cases in last 24 hours of the date searched for'
    #print(date_parameter)
    #print(casesWorld)
    print(world_wide_cases)
    print(continent_wide_cases)
    print(casesContinent)
}

worldwideCases("06/14/2020")
## [1] "Total cases worldwide till 06/14/2020 : 7766640"
## [1] "Number of cases in last 24 hours: Africa : 8037"  
## [2] "Number of cases in last 24 hours: America : 69850"
## [3] "Number of cases in last 24 hours: Asia : 38064"   
## [4] "Number of cases in last 24 hours: Europe : 17448" 
## [5] "Number of cases in last 24 hours: Oceania : 12"   
##   continentExp cases
## 1       Africa  8037
## 2      America 69850
## 3         Asia 38064
## 4       Europe 17448
## 5      Oceania    12
# formatted in a common way by which people enter the date: month-day-year

========================== x ======================================

Question 4)

Create visualizations that show the progression of the cases and the mortality rate in each continent.

Answer 4)

Visualisation:in each continent : to add population of each countries in that continent

1. progression of case: to add no. of cases of each countries in that continent

2. mortality rate: deaths/population: to add no. of deaths of each countries in that continent divide by population
# Mortality Rate is displayed in the scientific form rather than numeric. This is ? due to Population data is converted into intger type??? Checked and rechecked many times - that's the reason!! Quite surprising, weird and annoying! Converting it back to 'factor' and then, will use as.integer() in the codes.

#covid_entire$popData2018 <- as.factor(as.character(covid_entire$popData2018))

class(covid_entire$popData2018)
## [1] "numeric"
#covid_entire$popData2018 <- as.numeric(as.character(covid_entire$popData2018))
options(scipen = 999)
# Running this to stop formating values into 'scientific numbers'

continentMortality <- tibble(
  covid_entire %>% group_by(dateRep, continentExp) %>% 
    summarise(cases = sum(cases), deaths = sum(deaths), 
              population = sum(popData2018)) %>%
    mutate("mortalityRate" = ((deaths/population) * 100000)) %>% # 'denominator' for Mortality rate specific to any disease 
    select(dateRep, continentExp, cases, deaths, population, mortalityRate))
  
continentMortality
## # A tibble: 899 x 6
##    dateRep    continentExp cases deaths population mortalityRate
##    <fct>      <fct>        <dbl>  <dbl>      <dbl>         <dbl>
##  1 01/01/2020 Africa           0      0  336526764       0      
##  2 01/01/2020 America          0      0  727597933       0      
##  3 01/01/2020 Asia             0      0 3998054219       0      
##  4 01/01/2020 Europe           0      0  623974756       0      
##  5 01/01/2020 Oceania          0      0   29877869       0      
##  6 01/01/2020 Other            0      0       3000       0      
##  7 01/02/2020 Africa           0      0  336526764       0      
##  8 01/02/2020 America          2      0  727597933       0      
##  9 01/02/2020 Asia          2110     46 3998054219       0.00115
## 10 01/02/2020 Europe           6      0  623974756       0      
## # … with 889 more rows
#view(continentMortality)

# Ref: Mortality Rate: https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section3.html
#continentMortality %>% ggplot() + geom_point(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) + 
#geom_smooth(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) + facet_wrap(~ continentExp)


continentMortality %>% 
  ggplot(mapping = aes(y = mortalityRate, x = cases, color = continentExp)) + 
  geom_point() + geom_smooth(aes(color = mortalityRate)) + facet_wrap(~ continentExp) +
  ylim(0, 0.8) +
  labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population", 
       title = "Progression of Cases & Mortality Rate: Continent-wise") 
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at -0.67
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.4489
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.67
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.67
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger
## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_smooth).

         #subtitle = paste(":")

** ANALYSIS of Mortality Rate (per 100,000 population) and Progression of Cases: Continent-wise:

  1. Africa: Eventhough being the second largest continent, Africa has not crossed more than 10,000 cases per day. Mortality rate is controlled well under 0.05. Whether the numbers are less because of undocumented cases or les screening compared to other continents? Or it is so because of the warm climate? Other theory can also be because Africa has Malaria as endemic disease and people have consumed medicine Chloroquine that has been restricting or rather decreasing ‘virulence’ of the disease? Deeper studies required…

  2. America: This continent seems to be worst hit amongst all continents with number of cases goign as high as 80,000 per day. Currently USA, BRAZIL, PERU are amongst top 10 worst hit countries. The graph also suggests that, as number of cases increases, the mortality rate also increases. As the number of cases crosses 30,000 mark, the mortality rate remains between o.3 to 0.4. The highest mortality recorded for this continent is 0.55

  3. Asia: Considering that Asia is the largest continent and highest population compared to America and Europe continents, still the number of cases have not crossed 40,000 per day. Eventhough the disease started in Asia, relatively low number of cases than America and lower mortality rate (0.02-0.03 - similar to Africa) indicates well-controlled measures and management in containing the disease.

  4. Europe: Europe was the worst hit continent when the pandemic started spreading. The graph shows steep rise of mortality compared to America’s graph. America and Europe - both continents have almost similar mortality rate, but the major diofference is number of cases per day. That means with less number of cases, deaths were more - attributed to more senior population residing in Europe. That’s why the ‘virulence’ of the diseases seems more in Europe. This is more evident when we comapre Europe and Asia, where both continents have similar number of cases yet major difference is mortality rate (0.7 being the highest in Europe v/s 0.02-0.03 in Asia)

  5. Oceania: Australia and New Zealand contributing to major population of Oceania, but the countries are less densely populated makes common measure of social distancing possible. This seems to be the major contributor to control the disease and avoid spread. The continent seems to be less affected by the disease. New Zealand recently declared that it is free from the disease.

  6. Others: Japanese Cruise-ship: This graph is of the Japanese Cruise-ship that had COVID-19 positive patient and the disease spread in the ship. Total passengers plus crew were around 3000. The data suggests that the ship had very less mortality rate inspite of all patients and passengers were strangled inside the ship and affected around 700 passengers.

========================== x ======================================

Question 5)

Display the ten countries with the highest number of cases. Analyze the data and indicate the date of the first reported case and the ratio of cases-to-fatalities for each country (use supporting visualizations)

Answer 5)

topTen <- tibble(
  covid_entire %>% group_by(countriesAndTerritories) %>% 
    # group_by countries since we want top 10 countries
    summarise(cases = sum(cases), deaths = sum(deaths)) %>% 
    # both cases and deaths have to be totalled to calculate total number of cases/deaths till date
    mutate("case_to_fatalities" = ((deaths / cases)*100)) %>% 
    # adding new column in the tibble by calculating the Ratio of case-to-fatalities: generally considered as per 100: https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section3.html
    select(countriesAndTerritories, cases, deaths, case_to_fatalities))
    # selecting required columns

topTen <- head(topTen[order(topTen$cases, decreasing=T), ], 10)
# since we want to find ten countries having highest number of cases - using head() function and arranging total cases in decreasing order

topTen
## # A tibble: 10 x 4
##    countriesAndTerritories    cases deaths case_to_fatalities
##    <chr>                      <dbl>  <dbl>              <dbl>
##  1 United_States_of_America 2074526 115436               5.56
##  2 Brazil                    850514  42720               5.02
##  3 Russia                    520129   6829               1.31
##  4 India                     320922   9195               2.87
##  5 United_Kingdom            294900  41662              14.1 
##  6 Spain                     244690  29054              11.9 
##  7 Italy                     236651  34301              14.5 
##  8 Peru                      225132   6498               2.89
##  9 Germany                   186269   8787               4.72
## 10 Iran                      184955   8730               4.72
topTenCountries <- covid_entire[covid_entire$countriesAndTerritories %in% topTen$countriesAndTerritories,] 

topTenCountries$"case_to_fatalities" <- topTenCountries$deaths/topTenCountries$cases

#view(topTenCountries)

topTenCountries
##          dateRep day month year cases deaths  countriesAndTerritories
## 3051  14/06/2020  14     6 2020 21704    892                   Brazil
## 3052  13/06/2020  13     6 2020 25982    909                   Brazil
## 3053  12/06/2020  12     6 2020 30412   1239                   Brazil
## 3054  11/06/2020  11     6 2020 32913   1274                   Brazil
## 3055  10/06/2020  10     6 2020 32091   1272                   Brazil
## 3056  09/06/2020   9     6 2020 15654    679                   Brazil
## 3057  08/06/2020   8     6 2020 18921    525                   Brazil
## 3058  07/06/2020   7     6 2020 27075    904                   Brazil
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## 22487 10/05/2020  10     5 2020 25612   1614 United_States_of_America
## 22488 09/05/2020   9     5 2020 26957   1510 United_States_of_America
## 22489 08/05/2020   8     5 2020 28369   2239 United_States_of_America
## 22490 07/05/2020   7     5 2020 24128   2353 United_States_of_America
## 22491 06/05/2020   6     5 2020 23841   2144 United_States_of_America
## 22492 05/05/2020   5     5 2020 22593   1252 United_States_of_America
## 22493 04/05/2020   4     5 2020 24972   1297 United_States_of_America
## 22494 03/05/2020   3     5 2020 29288   1317 United_States_of_America
## 22495 02/05/2020   2     5 2020 33955   2062 United_States_of_America
## 22496 01/05/2020   1     5 2020 29917   2040 United_States_of_America
## 22497 30/04/2020  30     4 2020 27326   2611 United_States_of_America
## 22498 29/04/2020  29     4 2020 24132   2110 United_States_of_America
## 22499 28/04/2020  28     4 2020 22541   1369 United_States_of_America
## 22500 27/04/2020  27     4 2020 26857   1687 United_States_of_America
## 22501 26/04/2020  26     4 2020 48529   2172 United_States_of_America
## 22502 25/04/2020  25     4 2020 21352   1054 United_States_of_America
## 22503 24/04/2020  24     4 2020 26543   3179 United_States_of_America
## 22504 23/04/2020  23     4 2020 17588   1721 United_States_of_America
## 22505 22/04/2020  22     4 2020 37289   2524 United_States_of_America
## 22506 21/04/2020  21     4 2020 28065   1857 United_States_of_America
## 22507 20/04/2020  20     4 2020 24601   1772 United_States_of_America
## 22508 19/04/2020  19     4 2020 32922   1856 United_States_of_America
## 22509 18/04/2020  18     4 2020 30833   3770 United_States_of_America
## 22510 17/04/2020  17     4 2020 31667   2299 United_States_of_America
## 22511 16/04/2020  16     4 2020 30148   4928 United_States_of_America
## 22512 15/04/2020  15     4 2020 26922   2408 United_States_of_America
## 22513 14/04/2020  14     4 2020 25023   1541 United_States_of_America
## 22514 13/04/2020  13     4 2020 27620   1500 United_States_of_America
## 22515 12/04/2020  12     4 2020 28391   1831 United_States_of_America
## 22516 11/04/2020  11     4 2020 35527   2087 United_States_of_America
## 22517 10/04/2020  10     4 2020 33901   1873 United_States_of_America
## 22518 09/04/2020   9     4 2020 33323   1922 United_States_of_America
## 22519 08/04/2020   8     4 2020 30613   1906 United_States_of_America
## 22520 07/04/2020   7     4 2020 30561   1342 United_States_of_America
## 22521 06/04/2020   6     4 2020 25398   1146 United_States_of_America
## 22522 05/04/2020   5     4 2020 34272   1344 United_States_of_America
## 22523 04/04/2020   4     4 2020 32425   1104 United_States_of_America
## 22524 03/04/2020   3     4 2020 28819    915 United_States_of_America
## 22525 02/04/2020   2     4 2020 27103   1059 United_States_of_America
## 22526 01/04/2020   1     4 2020 24998    909 United_States_of_America
## 22527 31/03/2020  31     3 2020 21595    661 United_States_of_America
## 22528 30/03/2020  30     3 2020 18360    318 United_States_of_America
## 22529 29/03/2020  29     3 2020 19979    484 United_States_of_America
## 22530 28/03/2020  28     3 2020 18695    411 United_States_of_America
## 22531 27/03/2020  27     3 2020 16797    246 United_States_of_America
## 22532 26/03/2020  26     3 2020 13963    249 United_States_of_America
## 22533 25/03/2020  25     3 2020  8789    211 United_States_of_America
## 22534 24/03/2020  24     3 2020 11236    119 United_States_of_America
## 22535 23/03/2020  23     3 2020  8459    131 United_States_of_America
## 22536 22/03/2020  22     3 2020  7123     80 United_States_of_America
## 22537 21/03/2020  21     3 2020  5374    110 United_States_of_America
## 22538 20/03/2020  20     3 2020  4835      0 United_States_of_America
## 22539 19/03/2020  19     3 2020  2988     42 United_States_of_America
## 22540 18/03/2020  18     3 2020  1766     23 United_States_of_America
## 22541 17/03/2020  17     3 2020   887     16 United_States_of_America
## 22542 16/03/2020  16     3 2020   823     12 United_States_of_America
## 22543 15/03/2020  15     3 2020   777     10 United_States_of_America
## 22544 14/03/2020  14     3 2020   511      7 United_States_of_America
## 22545 13/03/2020  13     3 2020   351     10 United_States_of_America
## 22546 12/03/2020  12     3 2020   287      2 United_States_of_America
## 22547 11/03/2020  11     3 2020   271      2 United_States_of_America
## 22548 10/03/2020  10     3 2020   200      5 United_States_of_America
## 22549 09/03/2020   9     3 2020   121      4 United_States_of_America
## 22550 08/03/2020   8     3 2020    95      3 United_States_of_America
## 22551 07/03/2020   7     3 2020   105      2 United_States_of_America
## 22552 06/03/2020   6     3 2020    74      1 United_States_of_America
## 22553 05/03/2020   5     3 2020    34      2 United_States_of_America
## 22554 04/03/2020   4     3 2020    22      3 United_States_of_America
## 22555 03/03/2020   3     3 2020    14      4 United_States_of_America
## 22556 02/03/2020   2     3 2020    20      1 United_States_of_America
## 22557 01/03/2020   1     3 2020     3      1 United_States_of_America
## 22558 29/02/2020  29     2 2020     6      0 United_States_of_America
## 22559 28/02/2020  28     2 2020     1      0 United_States_of_America
## 22560 27/02/2020  27     2 2020     6      0 United_States_of_America
## 22561 26/02/2020  26     2 2020     0      0 United_States_of_America
## 22562 25/02/2020  25     2 2020    18      0 United_States_of_America
## 22563 24/02/2020  24     2 2020     0      0 United_States_of_America
## 22564 23/02/2020  23     2 2020     0      0 United_States_of_America
## 22565 22/02/2020  22     2 2020    19      0 United_States_of_America
## 22566 21/02/2020  21     2 2020     1      0 United_States_of_America
## 22567 20/02/2020  20     2 2020     0      0 United_States_of_America
## 22568 19/02/2020  19     2 2020     0      0 United_States_of_America
## 22569 18/02/2020  18     2 2020     0      0 United_States_of_America
## 22570 17/02/2020  17     2 2020     0      0 United_States_of_America
## 22571 16/02/2020  16     2 2020     0      0 United_States_of_America
## 22572 15/02/2020  15     2 2020     0      0 United_States_of_America
## 22573 14/02/2020  14     2 2020     1      0 United_States_of_America
## 22574 13/02/2020  13     2 2020     1      0 United_States_of_America
## 22575 12/02/2020  12     2 2020     0      0 United_States_of_America
## 22576 11/02/2020  11     2 2020     1      0 United_States_of_America
## 22577 10/02/2020  10     2 2020     0      0 United_States_of_America
## 22578 09/02/2020   9     2 2020     0      0 United_States_of_America
## 22579 08/02/2020   8     2 2020     0      0 United_States_of_America
## 22580 07/02/2020   7     2 2020     0      0 United_States_of_America
## 22581 06/02/2020   6     2 2020     1      0 United_States_of_America
## 22582 05/02/2020   5     2 2020     0      0 United_States_of_America
## 22583 04/02/2020   4     2 2020     0      0 United_States_of_America
## 22584 03/02/2020   3     2 2020     3      0 United_States_of_America
## 22585 02/02/2020   2     2 2020     1      0 United_States_of_America
## 22586 01/02/2020   1     2 2020     1      0 United_States_of_America
## 22587 31/01/2020  31     1 2020     1      0 United_States_of_America
## 22588 30/01/2020  30     1 2020     0      0 United_States_of_America
## 22589 29/01/2020  29     1 2020     0      0 United_States_of_America
## 22590 28/01/2020  28     1 2020     0      0 United_States_of_America
## 22591 27/01/2020  27     1 2020     3      0 United_States_of_America
## 22592 26/01/2020  26     1 2020     0      0 United_States_of_America
## 22593 25/01/2020  25     1 2020     1      0 United_States_of_America
## 22594 24/01/2020  24     1 2020     0      0 United_States_of_America
## 22595 23/01/2020  23     1 2020     0      0 United_States_of_America
## 22596 22/01/2020  22     1 2020     0      0 United_States_of_America
## 22597 21/01/2020  21     1 2020     1      0 United_States_of_America
## 22598 20/01/2020  20     1 2020     0      0 United_States_of_America
## 22599 19/01/2020  19     1 2020     0      0 United_States_of_America
## 22600 18/01/2020  18     1 2020     0      0 United_States_of_America
## 22601 17/01/2020  17     1 2020     0      0 United_States_of_America
## 22602 16/01/2020  16     1 2020     0      0 United_States_of_America
## 22603 15/01/2020  15     1 2020     0      0 United_States_of_America
## 22604 14/01/2020  14     1 2020     0      0 United_States_of_America
## 22605 13/01/2020  13     1 2020     0      0 United_States_of_America
## 22606 12/01/2020  12     1 2020     0      0 United_States_of_America
## 22607 11/01/2020  11     1 2020     0      0 United_States_of_America
## 22608 10/01/2020  10     1 2020     0      0 United_States_of_America
## 22609 09/01/2020   9     1 2020     0      0 United_States_of_America
## 22610 08/01/2020   8     1 2020     0      0 United_States_of_America
## 22611 07/01/2020   7     1 2020     0      0 United_States_of_America
## 22612 06/01/2020   6     1 2020     0      0 United_States_of_America
## 22613 05/01/2020   5     1 2020     0      0 United_States_of_America
## 22614 04/01/2020   4     1 2020     0      0 United_States_of_America
## 22615 03/01/2020   3     1 2020     0      0 United_States_of_America
## 22616 02/01/2020   2     1 2020     0      0 United_States_of_America
## 22617 01/01/2020   1     1 2020     0      0 United_States_of_America
## 22618 31/12/2019  31    12 2019     0      0 United_States_of_America
##       countryterritoryCode popData2018 continentExp case_to_fatalities
## 3051                   BRA   209469333      America       0.0410984150
## 3052                   BRA   209469333      America       0.0349857594
## 3053                   BRA   209469333      America       0.0407404972
## 3054                   BRA   209469333      America       0.0387081093
## 3055                   BRA   209469333      America       0.0396372815
## 3056                   BRA   209469333      America       0.0433754951
## 3057                   BRA   209469333      America       0.0277469478
## 3058                   BRA   209469333      America       0.0333887350
## 3059                   BRA   209469333      America       0.0325981187
## 3060                   BRA   209469333      America       0.0476452322
## 3061                   BRA   209469333      America       0.0471134705
## 3062                   BRA   209469333      America       0.0436134918
## 3063                   BRA   209469333      America       0.0537161580
## 3064                   BRA   209469333      America       0.0292522396
## 3065                   BRA   209469333      America       0.0287311414
## 3066                   BRA   209469333      America       0.0417409388
## 3067                   BRA   209469333      America       0.0437597002
## 3068                   BRA   209469333      America       0.0527210059
## 3069                   BRA   209469333      America       0.0636486155
## 3070                   BRA   209469333      America       0.0690510824
## 3071                   BRA   209469333      America       0.0412951369
## 3072                   BRA   209469333      America       0.0584565059
## 3073                   BRA   209469333      America       0.0481180599
## 3074                   BRA   209469333      America       0.0641884590
## 3075                   BRA   209469333      America       0.0445090472
## 3076                   BRA   209469333      America       0.0677274816
## 3077                   BRA   209469333      America       0.0512937595
## 3078                   BRA   209469333      America       0.0610985135
## 3079                   BRA   209469333      America       0.0546953549
## 3080                   BRA   209469333      America       0.0538386148
## 3081                   BRA   209469333      America       0.0605278256
## 3082                   BRA   209469333      America       0.0657883180
## 3083                   BRA   209469333      America       0.0951609419
## 3084                   BRA   209469333      America       0.0703125000
## 3085                   BRA   209469333      America       0.0733727811
## 3086                   BRA   209469333      America       0.0687965319
## 3087                   BRA   209469333      America       0.0734689885
## 3088                   BRA   209469333      America       0.0616909385
## 3089                   BRA   209469333      America       0.0585546987
## 3090                   BRA   209469333      America       0.0865176640
## 3091                   BRA   209469333      America       0.0446253581
## 3092                   BRA   209469333      America       0.0599389712
## 3093                   BRA   209469333      America       0.0847082495
## 3094                   BRA   209469333      America       0.0689321952
## 3095                   BRA   209469333      America       0.0602660017
## 3096                   BRA   209469333      America       0.0715423837
## 3097                   BRA   209469333      America       0.0880222841
## 3098                   BRA   209469333      America       0.0732711901
## 3099                   BRA   209469333      America       0.0559337082
## 3100                   BRA   209469333      America       0.0627493653
## 3101                   BRA   209469333      America       0.1019126463
## 3102                   BRA   209469333      America       0.1089692102
## 3103                   BRA   209469333      America       0.0616131441
## 3104                   BRA   209469333      America       0.0664531625
## 3105                   BRA   209469333      America       0.0586403736
## 3106                   BRA   209469333      America       0.0559610706
## 3107                   BRA   209469333      America       0.0706205005
## 3108                   BRA   209469333      America       0.0666257292
## 3109                   BRA   209469333      America       0.0893111639
## 3110                   BRA   209469333      America       0.0667102681
## 3111                   BRA   209469333      America       0.1113537118
## 3112                   BRA   209469333      America       0.0832672482
## 3113                   BRA   209469333      America       0.0686546463
## 3114                   BRA   209469333      America       0.0624426079
## 3115                   BRA   209469333      America       0.0645704660
## 3116                   BRA   209469333      America       0.0730569948
## 3117                   BRA   209469333      America       0.0601809955
## 3118                   BRA   209469333      America       0.0686333534
## 3119                   BRA   209469333      America       0.0723542117
## 3120                   BRA   209469333      America       0.0633802817
## 3121                   BRA   209469333      America       0.0597381342
## 3122                   BRA   209469333      America       0.0523560209
## 3123                   BRA   209469333      America       0.0540037244
## 3124                   BRA   209469333      America       0.0357462020
## 3125                   BRA   209469333      America       0.0369068541
## 3126                   BRA   209469333      America       0.0712074303
## 3127                   BRA   209469333      America       0.0625000000
## 3128                   BRA   209469333      America       0.0451745380
## 3129                   BRA   209469333      America       0.0298804781
## 3130                   BRA   209469333      America       0.0414937759
## 3131                   BRA   209469333      America       0.0474137931
## 3132                   BRA   209469333      America       0.0387096774
## 3133                   BRA   209469333      America       0.0260869565
## 3134                   BRA   209469333      America       0.0167464115
## 3135                   BRA   209469333      America       0.0312500000
## 3136                   BRA   209469333      America       0.0176678445
## 3137                   BRA   209469333      America       0.0103626943
## 3138                   BRA   209469333      America       0.0218978102
## 3139                   BRA   209469333      America       0.0175438596
## 3140                   BRA   209469333      America       0.0000000000
## 3141                   BRA   209469333      America       0.0000000000
## 3142                   BRA   209469333      America       0.0000000000
## 3143                   BRA   209469333      America       0.0000000000
## 3144                   BRA   209469333      America       0.0000000000
## 3145                   BRA   209469333      America       0.0000000000
## 3146                   BRA   209469333      America       0.0000000000
## 3147                   BRA   209469333      America                NaN
## 3148                   BRA   209469333      America       0.0000000000
## 3149                   BRA   209469333      America                NaN
## 3150                   BRA   209469333      America       0.0000000000
## 3151                   BRA   209469333      America       0.0000000000
## 3152                   BRA   209469333      America       0.0000000000
## 3153                   BRA   209469333      America                NaN
## 3154                   BRA   209469333      America                NaN
## 3155                   BRA   209469333      America                NaN
## 3156                   BRA   209469333      America       0.0000000000
## 3157                   BRA   209469333      America                NaN
## 3158                   BRA   209469333      America                NaN
## 3159                   BRA   209469333      America                NaN
## 3160                   BRA   209469333      America       0.0000000000
## 3161                   BRA   209469333      America                NaN
## 3162                   BRA   209469333      America                NaN
## 3163                   BRA   209469333      America                NaN
## 3164                   BRA   209469333      America                NaN
## 3165                   BRA   209469333      America                NaN
## 3166                   BRA   209469333      America                NaN
## 3167                   BRA   209469333      America                NaN
## 3168                   BRA   209469333      America                NaN
## 3169                   BRA   209469333      America                NaN
## 3170                   BRA   209469333      America                NaN
## 3171                   BRA   209469333      America                NaN
## 3172                   BRA   209469333      America                NaN
## 3173                   BRA   209469333      America                NaN
## 3174                   BRA   209469333      America                NaN
## 3175                   BRA   209469333      America                NaN
## 3176                   BRA   209469333      America                NaN
## 3177                   BRA   209469333      America                NaN
## 3178                   BRA   209469333      America                NaN
## 3179                   BRA   209469333      America                NaN
## 3180                   BRA   209469333      America                NaN
## 3181                   BRA   209469333      America                NaN
## 3182                   BRA   209469333      America                NaN
## 3183                   BRA   209469333      America                NaN
## 3184                   BRA   209469333      America                NaN
## 3185                   BRA   209469333      America                NaN
## 3186                   BRA   209469333      America                NaN
## 3187                   BRA   209469333      America                NaN
## 3188                   BRA   209469333      America                NaN
## 3189                   BRA   209469333      America                NaN
## 3190                   BRA   209469333      America                NaN
## 3191                   BRA   209469333      America                NaN
## 3192                   BRA   209469333      America                NaN
## 3193                   BRA   209469333      America                NaN
## 3194                   BRA   209469333      America                NaN
## 3195                   BRA   209469333      America                NaN
## 3196                   BRA   209469333      America                NaN
## 3197                   BRA   209469333      America                NaN
## 3198                   BRA   209469333      America                NaN
## 3199                   BRA   209469333      America                NaN
## 3200                   BRA   209469333      America                NaN
## 3201                   BRA   209469333      America                NaN
## 3202                   BRA   209469333      America                NaN
## 3203                   BRA   209469333      America                NaN
## 3204                   BRA   209469333      America                NaN
## 3205                   BRA   209469333      America                NaN
## 3206                   BRA   209469333      America                NaN
## 3207                   BRA   209469333      America                NaN
## 3208                   BRA   209469333      America                NaN
## 3209                   BRA   209469333      America                NaN
## 3210                   BRA   209469333      America                NaN
## 3211                   BRA   209469333      America                NaN
## 3212                   BRA   209469333      America                NaN
## 3213                   BRA   209469333      America                NaN
## 3214                   BRA   209469333      America                NaN
## 3215                   BRA   209469333      America                NaN
## 3216                   BRA   209469333      America                NaN
## 3217                   BRA   209469333      America                NaN
## 8325                   DEU    82927922       Europe       0.0242914980
## 8326                   DEU    82927922       Europe       0.0517241379
## 8327                   DEU    82927922       Europe       0.0310077519
## 8328                   DEU    82927922       Europe       0.0468468468
## 8329                   DEU    82927922       Europe       0.0566037736
## 8330                   DEU    82927922       Europe       0.1057142857
## 8331                   DEU    82927922       Europe       0.0280373832
## 8332                   DEU    82927922       Europe       0.0730897010
## 8333                   DEU    82927922       Europe       0.0810810811
## 8334                   DEU    82927922       Europe       0.0631163708
## 8335                   DEU    82927922       Europe       0.0761421320
## 8336                   DEU    82927922       Europe       0.0847953216
## 8337                   DEU    82927922       Europe       0.0516431925
## 8338                   DEU    82927922       Europe       0.0330330330
## 8339                   DEU    82927922       Europe       0.0384615385
## 8340                   DEU    82927922       Europe       0.0528455285
## 8341                   DEU    82927922       Europe       0.0526315789
## 8342                   DEU    82927922       Europe       0.1756373938
## 8343                   DEU    82927922       Europe       0.1298342541
## 8344                   DEU    82927922       Europe       0.1041666667
## 8345                   DEU    82927922       Europe       0.0346020761
## 8346                   DEU    82927922       Europe       0.0719257541
## 8347                   DEU    82927922       Europe       0.0658307210
## 8348                   DEU    82927922       Europe       0.0586956522
## 8349                   DEU    82927922       Europe       0.0765100671
## 8350                   DEU    82927922       Europe       0.1041405270
## 8351                   DEU    82927922       Europe       0.1403508772
## 8352                   DEU    82927922       Europe       0.0614035088
## 8353                   DEU    82927922       Europe       0.0566037736
## 8354                   DEU    82927922       Europe       0.0919354839
## 8355                   DEU    82927922       Europe       0.1106243154
## 8356                   DEU    82927922       Europe       0.0953912111
## 8357                   DEU    82927922       Europe       0.1265664160
## 8358                   DEU    82927922       Europe       0.1243301179
## 8359                   DEU    82927922       Europe       0.0616246499
## 8360                   DEU    82927922       Europe       0.0389805097
## 8361                   DEU    82927922       Europe       0.0823341327
## 8362                   DEU    82927922       Europe       0.1215880893
## 8363                   DEU    82927922       Europe       0.1030150754
## 8364                   DEU    82927922       Europe       0.1591128255
## 8365                   DEU    82927922       Europe       0.2029197080
## 8366                   DEU    82927922       Europe       0.0633284242
## 8367                   DEU    82927922       Europe       0.0933165195
## 8368                   DEU    82927922       Europe       0.1110681115
## 8369                   DEU    82927922       Europe                NaN
## 8370                   DEU    82927922       Europe       0.1170500677
## 8371                   DEU    82927922       Europe       0.1549079755
## 8372                   DEU    82927922       Europe       0.1424825175
## 8373                   DEU    82927922       Europe       0.1080550098
## 8374                   DEU    82927922       Europe       0.0805987334
## 8375                   DEU    82927922       Europe       0.0871046229
## 8376                   DEU    82927922       Europe       0.0971330766
## 8377                   DEU    82927922       Europe       0.0914115646
## 8378                   DEU    82927922       Europe       0.1256146625
## 8379                   DEU    82927922       Europe       0.1086834734
## 8380                   DEU    82927922       Europe       0.0619718310
## 8381                   DEU    82927922       Europe       0.0748576078
## 8382                   DEU    82927922       Europe       0.0670545858
## 8383                   DEU    82927922       Europe       0.0884615385
## 8384                   DEU    82927922       Europe       0.1099092812
## 8385                   DEU    82927922       Europe       0.1146419952
## 8386                   DEU    82927922       Europe       0.0816522574
## 8387                   DEU    82927922       Europe       0.0496649586
## 8388                   DEU    82927922       Europe       0.0457284651
## 8389                   DEU    82927922       Europe       0.0413743044
## 8390                   DEU    82927922       Europe       0.0499718204
## 8391                   DEU    82927922       Europe       0.0494571773
## 8392                   DEU    82927922       Europe       0.0634524107
## 8393                   DEU    82927922       Europe       0.0451225874
## 8394                   DEU    82927922       Europe       0.0250203971
## 8395                   DEU    82927922       Europe       0.0309973046
## 8396                   DEU    82927922       Europe       0.0231831634
## 8397                   DEU    82927922       Europe       0.0234855847
## 8398                   DEU    82927922       Europe       0.0227420403
## 8399                   DEU    82927922       Europe       0.0273244086
## 8400                   DEU    82927922       Europe       0.0277356446
## 8401                   DEU    82927922       Europe       0.0138918123
## 8402                   DEU    82927922       Europe       0.0161412358
## 8403                   DEU    82927922       Europe       0.0114394662
## 8404                   DEU    82927922       Europe       0.0095155709
## 8405                   DEU    82927922       Europe       0.0098909972
## 8406                   DEU    82927922       Europe       0.0098206661
## 8407                   DEU    82927922       Europe       0.0072104552
## 8408                   DEU    82927922       Europe       0.0081546361
## 8409                   DEU    82927922       Europe       0.0067155067
## 8410                   DEU    82927922       Europe       0.0004939491
## 8411                   DEU    82927922       Europe       0.0050505051
## 8412                   DEU    82927922       Europe       0.0000000000
## 8413                   DEU    82927922       Europe       0.0000000000
## 8414                   DEU    82927922       Europe       0.0008517888
## 8415                   DEU    82927922       Europe       0.0038350911
## 8416                   DEU    82927922       Europe       0.0040927694
## 8417                   DEU    82927922       Europe       0.0000000000
## 8418                   DEU    82927922       Europe       0.0024937656
## 8419                   DEU    82927922       Europe       0.0036900369
## 8420                   DEU    82927922       Europe       0.0000000000
## 8421                   DEU    82927922       Europe       0.0084388186
## 8422                   DEU    82927922       Europe       0.0000000000
## 8423                   DEU    82927922       Europe       0.0000000000
## 8424                   DEU    82927922       Europe       0.0000000000
## 8425                   DEU    82927922       Europe       0.0000000000
## 8426                   DEU    82927922       Europe       0.0000000000
## 8427                   DEU    82927922       Europe       0.0000000000
## 8428                   DEU    82927922       Europe       0.0000000000
## 8429                   DEU    82927922       Europe       0.0000000000
## 8430                   DEU    82927922       Europe       0.0000000000
## 8431                   DEU    82927922       Europe       0.0000000000
## 8432                   DEU    82927922       Europe       0.0000000000
## 8433                   DEU    82927922       Europe       0.0000000000
## 8434                   DEU    82927922       Europe       0.0000000000
## 8435                   DEU    82927922       Europe                NaN
## 8436                   DEU    82927922       Europe                NaN
## 8437                   DEU    82927922       Europe                NaN
## 8438                   DEU    82927922       Europe                NaN
## 8439                   DEU    82927922       Europe                NaN
## 8440                   DEU    82927922       Europe                NaN
## 8441                   DEU    82927922       Europe                NaN
## 8442                   DEU    82927922       Europe                NaN
## 8443                   DEU    82927922       Europe                NaN
## 8444                   DEU    82927922       Europe                NaN
## 8445                   DEU    82927922       Europe                NaN
## 8446                   DEU    82927922       Europe                NaN
## 8447                   DEU    82927922       Europe                NaN
## 8448                   DEU    82927922       Europe       0.0000000000
## 8449                   DEU    82927922       Europe                NaN
## 8450                   DEU    82927922       Europe                NaN
## 8451                   DEU    82927922       Europe                NaN
## 8452                   DEU    82927922       Europe       0.0000000000
## 8453                   DEU    82927922       Europe       0.0000000000
## 8454                   DEU    82927922       Europe                NaN
## 8455                   DEU    82927922       Europe                NaN
## 8456                   DEU    82927922       Europe       0.0000000000
## 8457                   DEU    82927922       Europe       0.0000000000
## 8458                   DEU    82927922       Europe       0.0000000000
## 8459                   DEU    82927922       Europe       0.0000000000
## 8460                   DEU    82927922       Europe       0.0000000000
## 8461                   DEU    82927922       Europe                NaN
## 8462                   DEU    82927922       Europe       0.0000000000
## 8463                   DEU    82927922       Europe       0.0000000000
## 8464                   DEU    82927922       Europe                NaN
## 8465                   DEU    82927922       Europe                NaN
## 8466                   DEU    82927922       Europe                NaN
## 8467                   DEU    82927922       Europe                NaN
## 8468                   DEU    82927922       Europe                NaN
## 8469                   DEU    82927922       Europe                NaN
## 8470                   DEU    82927922       Europe                NaN
## 8471                   DEU    82927922       Europe                NaN
## 8472                   DEU    82927922       Europe                NaN
## 8473                   DEU    82927922       Europe                NaN
## 8474                   DEU    82927922       Europe                NaN
## 8475                   DEU    82927922       Europe                NaN
## 8476                   DEU    82927922       Europe                NaN
## 8477                   DEU    82927922       Europe                NaN
## 8478                   DEU    82927922       Europe                NaN
## 8479                   DEU    82927922       Europe                NaN
## 8480                   DEU    82927922       Europe                NaN
## 8481                   DEU    82927922       Europe                NaN
## 8482                   DEU    82927922       Europe                NaN
## 8483                   DEU    82927922       Europe                NaN
## 8484                   DEU    82927922       Europe                NaN
## 8485                   DEU    82927922       Europe                NaN
## 8486                   DEU    82927922       Europe                NaN
## 8487                   DEU    82927922       Europe                NaN
## 8488                   DEU    82927922       Europe                NaN
## 8489                   DEU    82927922       Europe                NaN
## 8490                   DEU    82927922       Europe                NaN
## 8491                   DEU    82927922       Europe                NaN
## 10079                  IND  1352617328         Asia       0.0260709196
## 10080                  IND  1352617328         Asia       0.0336882527
## 10081                  IND  1352617328         Asia       0.0361445783
## 10082                  IND  1352617328         Asia       0.0357142857
## 10083                  IND  1352617328         Asia       0.0279419129
## 10084                  IND  1352617328         Asia       0.0331430860
## 10085                  IND  1352617328         Asia       0.0206350796
## 10086                  IND  1352617328         Asia       0.0287834721
## 10087                  IND  1352617328         Asia       0.0297360170
## 10088                  IND  1352617328         Asia       0.0277129225
## 10089                  IND  1352617328         Asia       0.0279449699
## 10090                  IND  1352617328         Asia       0.0243573914
## 10091                  IND  1352617328         Asia       0.0249663444
## 10092                  IND  1352617328         Asia       0.0274070543
## 10093                  IND  1352617328         Asia       0.0230310263
## 10094                  IND  1352617328         Asia       0.0332747363
## 10095                  IND  1352617328         Asia       0.0234395928
## 10096                  IND  1352617328         Asia       0.0295461468
## 10097                  IND  1352617328         Asia       0.0266165649
## 10098                  IND  1352617328         Asia       0.0223412395
## 10099                  IND  1352617328         Asia       0.0220725240
## 10100                  IND  1352617328         Asia       0.0217230678
## 10101                  IND  1352617328         Asia       0.0205891193
## 10102                  IND  1352617328         Asia       0.0243101183
## 10103                  IND  1352617328         Asia       0.0235336067
## 10104                  IND  1352617328         Asia       0.0249509891
## 10105                  IND  1352617328         Asia       0.0269617706
## 10106                  IND  1352617328         Asia       0.0299504006
## 10107                  IND  1352617328         Asia       0.0240625627
## 10108                  IND  1352617328         Asia       0.0259445844
## 10109                  IND  1352617328         Asia       0.0252079657
## 10110                  IND  1352617328         Asia       0.0360021494
## 10111                  IND  1352617328         Asia       0.0346099291
## 10112                  IND  1352617328         Asia       0.0241398446
## 10113                  IND  1352617328         Asia       0.0230239734
## 10114                  IND  1352617328         Asia       0.0390601160
## 10115                  IND  1352617328         Asia       0.0286144578
## 10116                  IND  1352617328         Asia       0.0303834808
## 10117                  IND  1352617328         Asia       0.0249929795
## 10118                  IND  1352617328         Asia       0.0425963489
## 10119                  IND  1352617328         Asia       0.0500000000
## 10120                  IND  1352617328         Asia       0.0282021152
## 10121                  IND  1352617328         Asia       0.0313918306
## 10122                  IND  1352617328         Asia       0.0309638029
## 10123                  IND  1352617328         Asia       0.0366281987
## 10124                  IND  1352617328         Asia       0.0389988359
## 10125                  IND  1352617328         Asia       0.0384818134
## 10126                  IND  1352617328         Asia       0.0401814647
## 10127                  IND  1352617328         Asia       0.0343839542
## 10128                  IND  1352617328         Asia       0.0246231156
## 10129                  IND  1352617328         Asia       0.0398880336
## 10130                  IND  1352617328         Asia       0.0219714964
## 10131                  IND  1352617328         Asia       0.0290986515
## 10132                  IND  1352617328         Asia       0.0361271676
## 10133                  IND  1352617328         Asia       0.0352059925
## 10134                  IND  1352617328         Asia       0.0231809401
## 10135                  IND  1352617328         Asia       0.0202398801
## 10136                  IND  1352617328         Asia       0.0433905146
## 10137                  IND  1352617328         Asia       0.0228401192
## 10138                  IND  1352617328         Asia       0.0392781316
## 10139                  IND  1352617328         Asia       0.0353488372
## 10140                  IND  1352617328         Asia       0.0255986788
## 10141                  IND  1352617328         Asia       0.0439698492
## 10142                  IND  1352617328         Asia       0.0374037404
## 10143                  IND  1352617328         Asia       0.0386473430
## 10144                  IND  1352617328         Asia       0.0486725664
## 10145                  IND  1352617328         Asia       0.0314814815
## 10146                  IND  1352617328         Asia       0.0452781371
## 10147                  IND  1352617328         Asia       0.0141242938
## 10148                  IND  1352617328         Asia       0.0461760462
## 10149                  IND  1352617328         Asia       0.0190677966
## 10150                  IND  1352617328         Asia       0.0199667221
## 10151                  IND  1352617328         Asia       0.0178571429
## 10152                  IND  1352617328         Asia       0.0264084507
## 10153                  IND  1352617328         Asia       0.0205479452
## 10154                  IND  1352617328         Asia       0.0166666667
## 10155                  IND  1352617328         Asia       0.0434782609
## 10156                  IND  1352617328         Asia       0.0566037736
## 10157                  IND  1352617328         Asia       0.0134228188
## 10158                  IND  1352617328         Asia       0.0533333333
## 10159                  IND  1352617328         Asia       0.0459770115
## 10160                  IND  1352617328         Asia       0.0000000000
## 10161                  IND  1352617328         Asia       0.0377358491
## 10162                  IND  1352617328         Asia       0.0252100840
## 10163                  IND  1352617328         Asia       0.0000000000
## 10164                  IND  1352617328         Asia       0.0000000000
## 10165                  IND  1352617328         Asia       0.0384615385
## 10166                  IND  1352617328         Asia       0.0000000000
## 10167                  IND  1352617328         Asia       0.0000000000
## 10168                  IND  1352617328         Asia       0.0312500000
## 10169                  IND  1352617328         Asia       0.0000000000
## 10170                  IND  1352617328         Asia       0.0000000000
## 10171                  IND  1352617328         Asia       0.1250000000
## 10172                  IND  1352617328         Asia       0.5000000000
## 10173                  IND  1352617328         Asia       0.0000000000
## 10174                  IND  1352617328         Asia       0.0000000000
## 10175                  IND  1352617328         Asia       0.0000000000
## 10176                  IND  1352617328         Asia       0.0000000000
## 10177                  IND  1352617328         Asia       0.0000000000
## 10178                  IND  1352617328         Asia       0.0000000000
## 10179                  IND  1352617328         Asia       0.0000000000
## 10180                  IND  1352617328         Asia       0.0000000000
## 10181                  IND  1352617328         Asia       0.0000000000
## 10182                  IND  1352617328         Asia                NaN
## 10183                  IND  1352617328         Asia                NaN
## 10184                  IND  1352617328         Asia                NaN
## 10185                  IND  1352617328         Asia                NaN
## 10186                  IND  1352617328         Asia                NaN
## 10187                  IND  1352617328         Asia                NaN
## 10188                  IND  1352617328         Asia                NaN
## 10189                  IND  1352617328         Asia                NaN
## 10190                  IND  1352617328         Asia                NaN
## 10191                  IND  1352617328         Asia                NaN
## 10192                  IND  1352617328         Asia                NaN
## 10193                  IND  1352617328         Asia                NaN
## 10194                  IND  1352617328         Asia                NaN
## 10195                  IND  1352617328         Asia                NaN
## 10196                  IND  1352617328         Asia                NaN
## 10197                  IND  1352617328         Asia                NaN
## 10198                  IND  1352617328         Asia                NaN
## 10199                  IND  1352617328         Asia                NaN
## 10200                  IND  1352617328         Asia                NaN
## 10201                  IND  1352617328         Asia                NaN
## 10202                  IND  1352617328         Asia                NaN
## 10203                  IND  1352617328         Asia                NaN
## 10204                  IND  1352617328         Asia                NaN
## 10205                  IND  1352617328         Asia                NaN
## 10206                  IND  1352617328         Asia                NaN
## 10207                  IND  1352617328         Asia                NaN
## 10208                  IND  1352617328         Asia                NaN
## 10209                  IND  1352617328         Asia       0.0000000000
## 10210                  IND  1352617328         Asia                NaN
## 10211                  IND  1352617328         Asia       0.0000000000
## 10212                  IND  1352617328         Asia                NaN
## 10213                  IND  1352617328         Asia                NaN
## 10214                  IND  1352617328         Asia       0.0000000000
## 10215                  IND  1352617328         Asia                NaN
## 10216                  IND  1352617328         Asia                NaN
## 10217                  IND  1352617328         Asia                NaN
## 10218                  IND  1352617328         Asia                NaN
## 10219                  IND  1352617328         Asia                NaN
## 10220                  IND  1352617328         Asia                NaN
## 10221                  IND  1352617328         Asia                NaN
## 10222                  IND  1352617328         Asia                NaN
## 10223                  IND  1352617328         Asia                NaN
## 10224                  IND  1352617328         Asia                NaN
## 10225                  IND  1352617328         Asia                NaN
## 10226                  IND  1352617328         Asia                NaN
## 10227                  IND  1352617328         Asia                NaN
## 10228                  IND  1352617328         Asia                NaN
## 10229                  IND  1352617328         Asia                NaN
## 10230                  IND  1352617328         Asia                NaN
## 10231                  IND  1352617328         Asia                NaN
## 10232                  IND  1352617328         Asia                NaN
## 10233                  IND  1352617328         Asia                NaN
## 10234                  IND  1352617328         Asia                NaN
## 10235                  IND  1352617328         Asia                NaN
## 10236                  IND  1352617328         Asia                NaN
## 10237                  IND  1352617328         Asia                NaN
## 10238                  IND  1352617328         Asia                NaN
## 10239                  IND  1352617328         Asia                NaN
## 10240                  IND  1352617328         Asia                NaN
## 10241                  IND  1352617328         Asia                NaN
## 10242                  IND  1352617328         Asia                NaN
## 10243                  IND  1352617328         Asia                NaN
## 10244                  IND  1352617328         Asia                NaN
## 10405                  IRN    81800269         Asia       0.0294605809
## 10406                  IRN    81800269         Asia       0.0316589278
## 10407                  IRN    81800269         Asia       0.0348525469
## 10408                  IRN    81800269         Asia       0.0402784684
## 10409                  IRN    81800269         Asia       0.0353221957
## 10410                  IRN    81800269         Asia       0.0342633382
## 10411                  IRN    81800269         Asia       0.0304568528
## 10412                  IRN    81800269         Asia       0.0330542089
## 10413                  IRN    81800269         Asia       0.0218295218
## 10414                  IRN    81800269         Asia       0.0165081142
## 10415                  IRN    81800269         Asia       0.0223356733
## 10416                  IRN    81800269         Asia       0.0205325634
## 10417                  IRN    81800269         Asia       0.0271903323
## 10418                  IRN    81800269         Asia       0.0250397456
## 10419                  IRN    81800269         Asia       0.0249780894
## 10420                  IRN    81800269         Asia       0.0177367861
## 10421                  IRN    81800269         Asia       0.0279007972
## 10422                  IRN    81800269         Asia       0.0269230769
## 10423                  IRN    81800269         Asia       0.0318970341
## 10424                  IRN    81800269         Asia       0.0168067227
## 10425                  IRN    81800269         Asia       0.0266055046
## 10426                  IRN    81800269         Asia       0.0315676833
## 10427                  IRN    81800269         Asia       0.0220683687
## 10428                  IRN    81800269         Asia       0.0275919732
## 10429                  IRN    81800269         Asia       0.0272804774
## 10430                  IRN    81800269         Asia       0.0293699668
## 10431                  IRN    81800269         Asia       0.0300784656
## 10432                  IRN    81800269         Asia       0.0282392027
## 10433                  IRN    81800269         Asia       0.0199203187
## 10434                  IRN    81800269         Asia       0.0228353949
## 10435                  IRN    81800269         Asia       0.0392699115
## 10436                  IRN    81800269         Asia       0.0255362615
## 10437                  IRN    81800269         Asia       0.0324105334
## 10438                  IRN    81800269         Asia       0.0267379679
## 10439                  IRN    81800269         Asia       0.0368763557
## 10440                  IRN    81800269         Asia       0.0313930674
## 10441                  IRN    81800269         Asia       0.0353470437
## 10442                  IRN    81800269         Asia       0.0457912458
## 10443                  IRN    81800269         Asia       0.0464285714
## 10444                  IRN    81800269         Asia       0.0476190476
## 10445                  IRN    81800269         Asia       0.0605069501
## 10446                  IRN    81800269         Asia       0.0481557377
## 10447                  IRN    81800269         Asia       0.0810473815
## 10448                  IRN    81800269         Asia       0.0626242545
## 10449                  IRN    81800269         Asia       0.0722278739
## 10450                  IRN    81800269         Asia       0.0745573159
## 10451                  IRN    81800269         Asia       0.0638489209
## 10452                  IRN    81800269         Asia       0.0968718466
## 10453                  IRN    81800269         Asia       0.0520381613
## 10454                  IRN    81800269         Asia       0.0670194004
## 10455                  IRN    81800269         Asia       0.0796232877
## 10456                  IRN    81800269         Asia       0.0873786408
## 10457                  IRN    81800269         Asia       0.0787269682
## 10458                  IRN    81800269         Asia       0.0678488820
## 10459                  IRN    81800269         Asia       0.0703245750
## 10460                  IRN    81800269         Asia       0.0647803425
## 10461                  IRN    81800269         Asia       0.0531295488
## 10462                  IRN    81800269         Asia       0.0593729153
## 10463                  IRN    81800269         Asia       0.0572851806
## 10464                  IRN    81800269         Asia       0.0621693122
## 10465                  IRN    81800269         Asia       0.0622617535
## 10466                  IRN    81800269         Asia       0.0686456401
## 10467                  IRN    81800269         Asia       0.0706095353
## 10468                  IRN    81800269         Asia       0.0680457267
## 10469                  IRN    81800269         Asia       0.0618661258
## 10470                  IRN    81800269         Asia       0.0716034272
## 10471                  IRN    81800269         Asia       0.0605908863
## 10472                  IRN    81800269         Asia       0.0636668262
## 10473                  IRN    81800269         Asia       0.0598065084
## 10474                  IRN    81800269         Asia       0.0608135320
## 10475                  IRN    81800269         Asia       0.0553554502
## 10476                  IRN    81800269         Asia                NaN
## 10477                  IRN    81800269         Asia       0.0431304348
## 10478                  IRN    81800269         Asia       0.0462002009
## 10479                  IRN    81800269         Asia       0.0453230473
## 10480                  IRN    81800269         Asia       0.0367231638
## 10481                  IRN    81800269         Asia       0.0423991727
## 10482                  IRN    81800269         Asia       0.0451885566
## 10483                  IRN    81800269         Asia       0.0492139440
## 10484                  IRN    81800269         Asia       0.0657178736
## 10485                  IRN    81800269         Asia       0.0648232094
## 10486                  IRN    81800269         Asia       0.0692395006
## 10487                  IRN    81800269         Asia       0.0900070872
## 10488                  IRN    81800269         Asia       0.1254863813
## 10489                  IRN    81800269         Asia       0.1273291925
## 10490                  IRN    81800269         Asia       0.1204527082
## 10491                  IRN    81800269         Asia       0.1424474187
## 10492                  IRN    81800269         Asia       0.1233221477
## 10493                  IRN    81800269         Asia       0.1146010187
## 10494                  IRN    81800269         Asia       0.1225071225
## 10495                  IRN    81800269         Asia       0.0934656741
## 10496                  IRN    81800269         Asia       0.0710622711
## 10497                  IRN    81800269         Asia       0.0659425912
## 10498                  IRN    81800269         Asia       0.0697674419
## 10499                  IRN    81800269         Asia       0.0657620042
## 10500                  IRN    81800269         Asia       0.0612939841
## 10501                  IRN    81800269         Asia       0.0722689076
## 10502                  IRN    81800269         Asia       0.0659488560
## 10503                  IRN    81800269         Asia       0.0195167286
## 10504                  IRN    81800269         Asia       0.0137763371
## 10505                  IRN    81800269         Asia       0.0253807107
## 10506                  IRN    81800269         Asia       0.0255972696
## 10507                  IRN    81800269         Asia       0.0131736527
## 10508                  IRN    81800269         Asia       0.0229445507
## 10509                  IRN    81800269         Asia       0.0285714286
## 10510                  IRN    81800269         Asia       0.0439024390
## 10511                  IRN    81800269         Asia       0.0559440559
## 10512                  IRN    81800269         Asia       0.0660377358
## 10513                  IRN    81800269         Asia       0.0909090909
## 10514                  IRN    81800269         Asia       0.0882352941
## 10515                  IRN    81800269         Asia       0.2222222222
## 10516                  IRN    81800269         Asia       0.2000000000
## 10517                  IRN    81800269         Asia       0.1000000000
## 10518                  IRN    81800269         Asia       0.1538461538
## 10519                  IRN    81800269         Asia       0.0000000000
## 10520                  IRN    81800269         Asia       1.0000000000
## 10521                  IRN    81800269         Asia                NaN
## 10522                  IRN    81800269         Asia                NaN
## 10523                  IRN    81800269         Asia                NaN
## 10524                  IRN    81800269         Asia                NaN
## 10525                  IRN    81800269         Asia                NaN
## 10526                  IRN    81800269         Asia                NaN
## 10527                  IRN    81800269         Asia                NaN
## 10528                  IRN    81800269         Asia                NaN
## 10529                  IRN    81800269         Asia                NaN
## 10530                  IRN    81800269         Asia                NaN
## 10531                  IRN    81800269         Asia                NaN
## 10532                  IRN    81800269         Asia                NaN
## 10533                  IRN    81800269         Asia                NaN
## 10534                  IRN    81800269         Asia                NaN
## 10535                  IRN    81800269         Asia                NaN
## 10536                  IRN    81800269         Asia                NaN
## 10537                  IRN    81800269         Asia                NaN
## 10538                  IRN    81800269         Asia                NaN
## 10539                  IRN    81800269         Asia                NaN
## 10540                  IRN    81800269         Asia                NaN
## 10541                  IRN    81800269         Asia                NaN
## 10542                  IRN    81800269         Asia                NaN
## 10543                  IRN    81800269         Asia                NaN
## 10544                  IRN    81800269         Asia                NaN
## 10545                  IRN    81800269         Asia                NaN
## 10546                  IRN    81800269         Asia                NaN
## 10547                  IRN    81800269         Asia                NaN
## 10548                  IRN    81800269         Asia                NaN
## 10549                  IRN    81800269         Asia                NaN
## 10550                  IRN    81800269         Asia                NaN
## 10551                  IRN    81800269         Asia                NaN
## 10552                  IRN    81800269         Asia                NaN
## 10553                  IRN    81800269         Asia                NaN
## 10554                  IRN    81800269         Asia                NaN
## 10555                  IRN    81800269         Asia                NaN
## 10556                  IRN    81800269         Asia                NaN
## 10557                  IRN    81800269         Asia                NaN
## 10558                  IRN    81800269         Asia                NaN
## 10559                  IRN    81800269         Asia                NaN
## 10560                  IRN    81800269         Asia                NaN
## 10561                  IRN    81800269         Asia                NaN
## 10562                  IRN    81800269         Asia                NaN
## 10563                  IRN    81800269         Asia                NaN
## 10564                  IRN    81800269         Asia                NaN
## 10565                  IRN    81800269         Asia                NaN
## 10566                  IRN    81800269         Asia                NaN
## 10567                  IRN    81800269         Asia                NaN
## 10568                  IRN    81800269         Asia                NaN
## 10569                  IRN    81800269         Asia                NaN
## 10570                  IRN    81800269         Asia                NaN
## 10571                  IRN    81800269         Asia                NaN
## 11152                  ITA    60431283       Europe       0.2254335260
## 11153                  ITA    60431283       Europe       0.3435582822
## 11154                  ITA    60431283       Europe       0.1398416887
## 11155                  ITA    60431283       Europe       0.3514851485
## 11156                  ITA    60431283       Europe       0.2791519435
## 11157                  ITA    60431283       Europe       0.2321428571
## 11158                  ITA    60431283       Europe       0.2690355330
## 11159                  ITA    60431283       Europe       0.2666666667
## 11160                  ITA    60431283       Europe       0.1640926641
## 11161                  ITA    60431283       Europe       0.4971751412
## 11162                  ITA    60431283       Europe       0.2211838006
## 11163                  ITA    60431283       Europe       0.1729559748
## 11164                  ITA    60431283       Europe       0.3370786517
## 11165                  ITA    60431283       Europe       0.2112676056
## 11166                  ITA    60431283       Europe       0.2668269231
## 11167                  ITA    60431283       Europe       0.1686046512
## 11168                  ITA    60431283       Europe       0.1180438449
## 11169                  ITA    60431283       Europe       0.2003424658
## 11170                  ITA    60431283       Europe       0.1964735516
## 11171                  ITA    60431283       Europe       0.3066666667
## 11172                  ITA    60431283       Europe       0.0941619586
## 11173                  ITA    60431283       Europe       0.1778774290
## 11174                  ITA    60431283       Europe       0.1993865031
## 11175                  ITA    60431283       Europe       0.2429906542
## 11176                  ITA    60431283       Europe       0.2421052632
## 11177                  ITA    60431283       Europe       0.1992619926
## 11178                  ITA    60431283       Europe       0.2195121951
## 11179                  ITA    60431283       Europe       0.2148148148
## 11180                  ITA    60431283       Europe       0.1748571429
## 11181                  ITA    60431283       Europe       0.3067173638
## 11182                  ITA    60431283       Europe       0.2641129032
## 11183                  ITA    60431283       Europe       0.2195945946
## 11184                  ITA    60431283       Europe       0.1226818830
## 11185                  ITA    60431283       Europe       0.2405913978
## 11186                  ITA    60431283       Europe       0.2057356608
## 11187                  ITA    60431283       Europe       0.1791320406
## 11188                  ITA    60431283       Europe       0.1831198191
## 11189                  ITA    60431283       Europe       0.1955745896
## 11190                  ITA    60431283       Europe       0.2555401662
## 11191                  ITA    60431283       Europe       0.2195348837
## 11192                  ITA    60431283       Europe       0.1597051597
## 11193                  ITA    60431283       Europe       0.1252699784
## 11194                  ITA    60431283       Europe       0.2494736842
## 11195                  ITA    60431283       Europe       0.1368956743
## 11196                  ITA    60431283       Europe       0.1522435897
## 11197                  ITA    60431283       Europe       0.1548418025
## 11198                  ITA    60431283       Europe       0.1826877092
## 11199                  ITA    60431283       Europe       0.1914893617
## 11200                  ITA    60431283       Europe       0.1118760757
## 11201                  ITA    60431283       Europe       0.1760712770
## 11202                  ITA    60431283       Europe       0.1390268123
## 11203                  ITA    60431283       Europe       0.1753590325
## 11204                  ITA    60431283       Europe       0.1296735905
## 11205                  ITA    60431283       Europe       0.1956760718
## 11206                  ITA    60431283       Europe       0.2012411348
## 11207                  ITA    60431283       Europe       0.1421069905
## 11208                  ITA    60431283       Europe       0.1374964194
## 11209                  ITA    60431283       Europe       0.1646149442
## 11210                  ITA    60431283       Europe       0.1386687797
## 11211                  ITA    60431283       Europe       0.2167229096
## 11212                  ITA    60431283       Europe       0.2032301480
## 11213                  ITA    60431283       Europe       0.1788772598
## 11214                  ITA    60431283       Europe       0.1053274682
## 11215                  ITA    60431283       Europe       0.1318704729
## 11216                  ITA    60431283       Europe       0.1442672741
## 11217                  ITA    60431283       Europe       0.1455756422
## 11218                  ITA    60431283       Europe       0.1407716371
## 11219                  ITA    60431283       Europe       0.1987495887
## 11220                  ITA    60431283       Europe       0.1767157544
## 11221                  ITA    60431283       Europe       0.1221037998
## 11222                  ITA    60431283       Europe       0.1417273673
## 11223                  ITA    60431283       Europe       0.1666303162
## 11224                  ITA    60431283       Europe       0.1628106255
## 11225                  ITA    60431283       Europe       0.1520284400
## 11226                  ITA    60431283       Europe       0.2070071552
## 11227                  ITA    60431283       Europe       0.2000000000
## 11228                  ITA    60431283       Europe       0.1452942304
## 11229                  ITA    60431283       Europe       0.1484767325
## 11230                  ITA    60431283       Europe       0.1629468032
## 11231                  ITA    60431283       Europe       0.1072647489
## 11232                  ITA    60431283       Europe       0.1314779271
## 11233                  ITA    60431283       Europe       0.1415507716
## 11234                  ITA    60431283       Europe       0.1254959282
## 11235                  ITA    60431283       Europe       0.1167266187
## 11236                  ITA    60431283       Europe       0.1212444716
## 11237                  ITA    60431283       Europe       0.1044102907
## 11238                  ITA    60431283       Europe       0.0806087937
## 11239                  ITA    60431283       Europe       0.1124316615
## 11240                  ITA    60431283       Europe       0.0984117981
## 11241                  ITA    60431283       Europe       0.0867500000
## 11242                  ITA    60431283       Europe       0.1310662416
## 11243                  ITA    60431283       Europe       0.0494709751
## 11244                  ITA    60431283       Europe       0.0989399293
## 11245                  ITA    60431283       Europe       0.0712938514
## 11246                  ITA    60431283       Europe       0.0847384349
## 11247                  ITA    60431283       Europe       0.1709314227
## 11248                  ITA    60431283       Europe       0.0545353367
## 11249                  ITA    60431283       Europe       0.0891420912
## 11250                  ITA    60431283       Europe       0.0288692863
## 11251                  ITA    60431283       Europe       0.0629820051
## 11252                  ITA    60431283       Europe       0.0533159948
## 11253                  ITA    60431283       Europe       0.0459965928
## 11254                  ITA    60431283       Europe       0.0600858369
## 11255                  ITA    60431283       Europe       0.0489913545
## 11256                  ITA    60431283       Europe       0.0106951872
## 11257                  ITA    60431283       Europe       0.0333333333
## 11258                  ITA    60431283       Europe       0.0168067227
## 11259                  ITA    60431283       Europe       0.0200000000
## 11260                  ITA    60431283       Europe       0.0128205128
## 11261                  ITA    60431283       Europe       0.0537634409
## 11262                  ITA    60431283       Europe       0.0412371134
## 11263                  ITA    60431283       Europe       0.0000000000
## 11264                  ITA    60431283       Europe       0.0322580645
## 11265                  ITA    60431283       Europe       0.0000000000
## 11266                  ITA    60431283       Europe                NaN
## 11267                  ITA    60431283       Europe                NaN
## 11268                  ITA    60431283       Europe                NaN
## 11269                  ITA    60431283       Europe                NaN
## 11270                  ITA    60431283       Europe                NaN
## 11271                  ITA    60431283       Europe                NaN
## 11272                  ITA    60431283       Europe                NaN
## 11273                  ITA    60431283       Europe                NaN
## 11274                  ITA    60431283       Europe                NaN
## 11275                  ITA    60431283       Europe                NaN
## 11276                  ITA    60431283       Europe                NaN
## 11277                  ITA    60431283       Europe                NaN
## 11278                  ITA    60431283       Europe                NaN
## 11279                  ITA    60431283       Europe                NaN
## 11280                  ITA    60431283       Europe                NaN
## 11281                  ITA    60431283       Europe                NaN
## 11282                  ITA    60431283       Europe                NaN
## 11283                  ITA    60431283       Europe                NaN
## 11284                  ITA    60431283       Europe                NaN
## 11285                  ITA    60431283       Europe                NaN
## 11286                  ITA    60431283       Europe                NaN
## 11287                  ITA    60431283       Europe       0.0000000000
## 11288                  ITA    60431283       Europe                NaN
## 11289                  ITA    60431283       Europe                NaN
## 11290                  ITA    60431283       Europe                NaN
## 11291                  ITA    60431283       Europe                NaN
## 11292                  ITA    60431283       Europe                NaN
## 11293                  ITA    60431283       Europe                NaN
## 11294                  ITA    60431283       Europe                NaN
## 11295                  ITA    60431283       Europe                NaN
## 11296                  ITA    60431283       Europe                NaN
## 11297                  ITA    60431283       Europe                NaN
## 11298                  ITA    60431283       Europe                NaN
## 11299                  ITA    60431283       Europe                NaN
## 11300                  ITA    60431283       Europe                NaN
## 11301                  ITA    60431283       Europe                NaN
## 11302                  ITA    60431283       Europe                NaN
## 11303                  ITA    60431283       Europe                NaN
## 11304                  ITA    60431283       Europe                NaN
## 11305                  ITA    60431283       Europe                NaN
## 11306                  ITA    60431283       Europe                NaN
## 11307                  ITA    60431283       Europe                NaN
## 11308                  ITA    60431283       Europe                NaN
## 11309                  ITA    60431283       Europe                NaN
## 11310                  ITA    60431283       Europe                NaN
## 11311                  ITA    60431283       Europe                NaN
## 11312                  ITA    60431283       Europe                NaN
## 11313                  ITA    60431283       Europe                NaN
## 11314                  ITA    60431283       Europe                NaN
## 11315                  ITA    60431283       Europe                NaN
## 11316                  ITA    60431283       Europe                NaN
## 11317                  ITA    60431283       Europe                NaN
## 11318                  ITA    60431283       Europe                NaN
## 17061                  PER    31989256      America       0.0433493041
## 17062                  PER    31989256      America       0.0333836605
## 17063                  PER    31989256      America       0.0345347863
## 17064                  PER    31989256      America       0.0324356202
## 17065                  PER    31989256      America       0.0413366337
## 17066                  PER    31989256      America       0.0333228544
## 17067                  PER    31989256      America       0.0344755098
## 17068                  PER    31989256      America       0.0318953648
## 17069                  PER    31989256      America       0.0311756307
## 17070                  PER    31989256      America       0.0319794585
## 17071                  PER    31989256      America       0.0292957746
## 17072                  PER    31989256      America                NaN
## 17073                  PER    31989256      America       0.0230091677
## 17074                  PER    31989256      America       0.0153321976
## 17075                  PER    31989256      America       0.0190901706
## 17076                  PER    31989256      America       0.0201352598
## 17077                  PER    31989256      America       0.0197480422
## 17078                  PER    31989256      America       0.0316867078
## 17079                  PER    31989256      America       0.0275467775
## 17080                  PER    31989256      America       0.0430348259
## 17081                  PER    31989256      America       0.0197384067
## 17082                  PER    31989256      America       0.0318047337
## 17083                  PER    31989256      America       0.0327756914
## 17084                  PER    31989256      America       0.0261107602
## 17085                  PER    31989256      America       0.0242450959
## 17086                  PER    31989256      America       0.0274725275
## 17087                  PER    31989256      America       0.0530075188
## 17088                  PER    31989256      America       0.0334941050
## 17089                  PER    31989256      America       0.0321304993
## 17090                  PER    31989256      America       0.0323824210
## 17091                  PER    31989256      America       0.0228013029
## 17092                  PER    31989256      America       0.0263715564
## 17093                  PER    31989256      America       0.0296570899
## 17094                  PER    31989256      America       0.0475247525
## 17095                  PER    31989256      America       0.0327225131
## 17096                  PER    31989256      America       0.0315656566
## 17097                  PER    31989256      America       0.0261969286
## 17098                  PER    31989256      America       0.0253437584
## 17099                  PER    31989256      America       0.0245314223
## 17100                  PER    31989256      America       0.0261985853
## 17101                  PER    31989256      America       0.0401662050
## 17102                  PER    31989256      America       0.0253388332
## 17103                  PER    31989256      America       0.0366265060
## 17104                  PER    31989256      America       0.0209589434
## 17105                  PER    31989256      America       0.0354679803
## 17106                  PER    31989256      America       0.0324699015
## 17107                  PER    31989256      America       0.0289040546
## 17108                  PER    31989256      America       0.0456852792
## 17109                  PER    31989256      America       0.0128087832
## 17110                  PER    31989256      America       0.0179201738
## 17111                  PER    31989256      America       0.0844686649
## 17112                  PER    31989256      America       0.0252403846
## 17113                  PER    31989256      America       0.0325548478
## 17114                  PER    31989256      America       0.0257936508
## 17115                  PER    31989256      America       0.0645624103
## 17116                  PER    31989256      America       0.0430463576
## 17117                  PER    31989256      America       0.0515574651
## 17118                  PER    31989256      America       0.0260521042
## 17119                  PER    31989256      America       0.0196850394
## 17120                  PER    31989256      America       0.0204778157
## 17121                  PER    31989256      America       0.0132902299
## 17122                  PER    31989256      America                NaN
## 17123                  PER    31989256      America       0.0178837556
## 17124                  PER    31989256      America       0.0126182965
## 17125                  PER    31989256      America       0.0483619345
## 17126                  PER    31989256      America       0.0185995624
## 17127                  PER    31989256      America       0.0100864553
## 17128                  PER    31989256      America       0.0381679389
## 17129                  PER    31989256      America       0.0321428571
## 17130                  PER    31989256      America       0.0186915888
## 17131                  PER    31989256      America       0.0794701987
## 17132                  PER    31989256      America       0.0331491713
## 17133                  PER    31989256      America       0.0879120879
## 17134                  PER    31989256      America       0.0658914729
## 17135                  PER    31989256      America       0.0521739130
## 17136                  PER    31989256      America       0.0612244898
## 17137                  PER    31989256      America       0.0110497238
## 17138                  PER    31989256      America       0.1388888889
## 17139                  PER    31989256      America       0.0363636364
## 17140                  PER    31989256      America       0.0454545455
## 17141                  PER    31989256      America       0.0070422535
## 17142                  PER    31989256      America       0.0952380952
## 17143                  PER    31989256      America       0.0000000000
## 17144                  PER    31989256      America       0.0000000000
## 17145                  PER    31989256      America       0.0363636364
## 17146                  PER    31989256      America       0.0344827586
## 17147                  PER    31989256      America       0.0224719101
## 17148                  PER    31989256      America       0.0000000000
## 17149                  PER    31989256      America       0.0000000000
## 17150                  PER    31989256      America       0.0000000000
## 17151                  PER    31989256      America       0.0000000000
## 17152                  PER    31989256      America       0.0000000000
## 17153                  PER    31989256      America       0.0000000000
## 17154                  PER    31989256      America       0.0000000000
## 17155                  PER    31989256      America       0.0000000000
## 17156                  PER    31989256      America       0.0000000000
## 17157                  PER    31989256      America       0.0000000000
## 17158                  PER    31989256      America       0.0000000000
## 17159                  PER    31989256      America       0.0000000000
## 17935                  RUS   144478050       Europe       0.0130944176
## 17936                  RUS   144478050       Europe       0.0203627462
## 17937                  RUS   144478050       Europe       0.0198200251
## 17938                  RUS   144478050       Europe       0.0258210376
## 17939                  RUS   144478050       Europe       0.0197789412
## 17940                  RUS   144478050       Europe       0.0124652198
## 17941                  RUS   144478050       Europe       0.0149154052
## 17942                  RUS   144478050       Europe       0.0222473179
## 17943                  RUS   144478050       Europe       0.0165024066
## 17944                  RUS   144478050       Europe       0.0191371306
## 17945                  RUS   144478050       Europe       0.0208528585
## 17946                  RUS   144478050       Europe       0.0205348076
## 17947                  RUS   144478050       Europe       0.0179302712
## 17948                  RUS   144478050       Europe       0.0148899439
## 17949                  RUS   144478050       Europe       0.0202189455
## 17950                  RUS   144478050       Europe       0.0270648623
## 17951                  RUS   144478050       Europe       0.0207860471
## 17952                  RUS   144478050       Europe       0.0193091869
## 17953                  RUS   144478050       Europe       0.0195176669
## 17954                  RUS   144478050       Europe       0.0102839258
## 17955                  RUS   144478050       Europe       0.0177927666
## 17956                  RUS   144478050       Europe       0.0147339411
## 17957                  RUS   144478050       Europe       0.0168653025
## 17958                  RUS   144478050       Europe       0.0143519042
## 17959                  RUS   144478050       Europe       0.0154039251
## 17960                  RUS   144478050       Europe       0.0124149843
## 17961                  RUS   144478050       Europe       0.0101949361
## 17962                  RUS   144478050       Europe       0.0096817386
## 17963                  RUS   144478050       Europe       0.0129347826
## 17964                  RUS   144478050       Europe       0.0106623891
## 17965                  RUS   144478050       Europe       0.0093242430
## 17966                  RUS   144478050       Europe       0.0095731951
## 17967                  RUS   144478050       Europe       0.0098174144
## 17968                  RUS   144478050       Europe       0.0080645161
## 17969                  RUS   144478050       Europe       0.0079912822
## 17970                  RUS   144478050       Europe       0.0096144957
## 17971                  RUS   144478050       Europe       0.0091597346
## 17972                  RUS   144478050       Europe       0.0078354554
## 17973                  RUS   144478050       Europe       0.0081447107
## 17974                  RUS   144478050       Europe       0.0094040784
## 17975                  RUS   144478050       Europe       0.0071826859
## 17976                  RUS   144478050       Europe       0.0054547164
## 17977                  RUS   144478050       Europe       0.0055076380
## 17978                  RUS   144478050       Europe       0.0121013488
## 17979                  RUS   144478050       Europe       0.0142273560
## 17980                  RUS   144478050       Europe       0.0179763739
## 17981                  RUS   144478050       Europe       0.0112306972
## 17982                  RUS   144478050       Europe       0.0077444337
## 17983                  RUS   144478050       Europe       0.0103757271
## 17984                  RUS   144478050       Europe       0.0110626886
## 17985                  RUS   144478050       Europe       0.0102581638
## 17986                  RUS   144478050       Europe       0.0087976540
## 17987                  RUS   144478050       Europe       0.0108861727
## 17988                  RUS   144478050       Europe       0.0090393477
## 17989                  RUS   144478050       Europe       0.0103092784
## 17990                  RUS   144478050       Europe       0.0079207921
## 17991                  RUS   144478050       Europe       0.0083594566
## 17992                  RUS   144478050       Europe       0.0100737101
## 17993                  RUS   144478050       Europe       0.0098607889
## 17994                  RUS   144478050       Europe       0.0082644628
## 17995                  RUS   144478050       Europe       0.0079307859
## 17996                  RUS   144478050       Europe       0.0070367475
## 17997                  RUS   144478050       Europe       0.0109789570
## 17998                  RUS   144478050       Europe       0.0071985603
## 17999                  RUS   144478050       Europe       0.0100783875
## 18000                  RUS   144478050       Europe       0.0089102125
## 18001                  RUS   144478050       Europe       0.0042553191
## 18002                  RUS   144478050       Europe       0.0095320624
## 18003                  RUS   144478050       Europe       0.0020964361
## 18004                  RUS   144478050       Europe       0.0030395137
## 18005                  RUS   144478050       Europe       0.0154639175
## 18006                  RUS   144478050       Europe       0.0066555740
## 18007                  RUS   144478050       Europe       0.0077821012
## 18008                  RUS   144478050       Europe       0.0159090909
## 18009                  RUS   144478050       Europe       0.0139720559
## 18010                  RUS   144478050       Europe       0.0066225166
## 18011                  RUS   144478050       Europe       0.0111111111
## 18012                  RUS   144478050       Europe       0.0043859649
## 18013                  RUS   144478050       Europe       0.0102040816
## 18014                  RUS   144478050       Europe       0.0109890110
## 18015                  RUS   144478050       Europe       0.0000000000
## 18016                  RUS   144478050       Europe       0.0000000000
## 18017                  RUS   144478050       Europe                NaN
## 18018                  RUS   144478050       Europe       0.0000000000
## 18019                  RUS   144478050       Europe       0.0000000000
## 18020                  RUS   144478050       Europe       0.0000000000
## 18021                  RUS   144478050       Europe       0.0000000000
## 18022                  RUS   144478050       Europe       0.0000000000
## 18023                  RUS   144478050       Europe       0.0000000000
## 18024                  RUS   144478050       Europe       0.0000000000
## 18025                  RUS   144478050       Europe       0.0000000000
## 18026                  RUS   144478050       Europe       0.0000000000
## 18027                  RUS   144478050       Europe       0.0000000000
## 18028                  RUS   144478050       Europe       0.0000000000
## 18029                  RUS   144478050       Europe       0.0000000000
## 18030                  RUS   144478050       Europe                NaN
## 18031                  RUS   144478050       Europe                NaN
## 18032                  RUS   144478050       Europe                NaN
## 18033                  RUS   144478050       Europe                NaN
## 18034                  RUS   144478050       Europe       0.0000000000
## 18035                  RUS   144478050       Europe                NaN
## 18036                  RUS   144478050       Europe                NaN
## 18037                  RUS   144478050       Europe       0.0000000000
## 18038                  RUS   144478050       Europe       0.0000000000
## 18039                  RUS   144478050       Europe                NaN
## 18040                  RUS   144478050       Europe                NaN
## 18041                  RUS   144478050       Europe                NaN
## 18042                  RUS   144478050       Europe                NaN
## 18043                  RUS   144478050       Europe                NaN
## 18044                  RUS   144478050       Europe                NaN
## 18045                  RUS   144478050       Europe                NaN
## 18046                  RUS   144478050       Europe                NaN
## 18047                  RUS   144478050       Europe                NaN
## 18048                  RUS   144478050       Europe                NaN
## 18049                  RUS   144478050       Europe                NaN
## 18050                  RUS   144478050       Europe                NaN
## 18051                  RUS   144478050       Europe                NaN
## 18052                  RUS   144478050       Europe                NaN
## 18053                  RUS   144478050       Europe                NaN
## 18054                  RUS   144478050       Europe                NaN
## 18055                  RUS   144478050       Europe                NaN
## 18056                  RUS   144478050       Europe                NaN
## 18057                  RUS   144478050       Europe                NaN
## 18058                  RUS   144478050       Europe                NaN
## 18059                  RUS   144478050       Europe                NaN
## 18060                  RUS   144478050       Europe                NaN
## 18061                  RUS   144478050       Europe                NaN
## 18062                  RUS   144478050       Europe                NaN
## 18063                  RUS   144478050       Europe                NaN
## 18064                  RUS   144478050       Europe                NaN
## 18065                  RUS   144478050       Europe                NaN
## 18066                  RUS   144478050       Europe                NaN
## 18067                  RUS   144478050       Europe                NaN
## 18068                  RUS   144478050       Europe                NaN
## 18069                  RUS   144478050       Europe       0.0000000000
## 18070                  RUS   144478050       Europe                NaN
## 18071                  RUS   144478050       Europe                NaN
## 18072                  RUS   144478050       Europe                NaN
## 18073                  RUS   144478050       Europe                NaN
## 18074                  RUS   144478050       Europe                NaN
## 18075                  RUS   144478050       Europe                NaN
## 18076                  RUS   144478050       Europe                NaN
## 18077                  RUS   144478050       Europe                NaN
## 18078                  RUS   144478050       Europe                NaN
## 18079                  RUS   144478050       Europe                NaN
## 18080                  RUS   144478050       Europe                NaN
## 18081                  RUS   144478050       Europe                NaN
## 18082                  RUS   144478050       Europe                NaN
## 18083                  RUS   144478050       Europe                NaN
## 18084                  RUS   144478050       Europe                NaN
## 18085                  RUS   144478050       Europe                NaN
## 18086                  RUS   144478050       Europe                NaN
## 18087                  RUS   144478050       Europe                NaN
## 18088                  RUS   144478050       Europe                NaN
## 18089                  RUS   144478050       Europe                NaN
## 18090                  RUS   144478050       Europe                NaN
## 18091                  RUS   144478050       Europe                NaN
## 18092                  RUS   144478050       Europe                NaN
## 18093                  RUS   144478050       Europe                NaN
## 18094                  RUS   144478050       Europe                NaN
## 18095                  RUS   144478050       Europe                NaN
## 18096                  RUS   144478050       Europe                NaN
## 18097                  RUS   144478050       Europe                NaN
## 18098                  RUS   144478050       Europe                NaN
## 18099                  RUS   144478050       Europe                NaN
## 18100                  RUS   144478050       Europe                NaN
## 18101                  RUS   144478050       Europe                NaN
## 20019                  ESP    46723749       Europe       0.0000000000
## 20020                  ESP    46723749       Europe       0.0000000000
## 20021                  ESP    46723749       Europe       0.0000000000
## 20022                  ESP    46723749       Europe       0.0000000000
## 20023                  ESP    46723749       Europe       0.0000000000
## 20024                  ESP    46723749       Europe       0.0000000000
## 20025                  ESP    46723749       Europe       0.0041666667
## 20026                  ESP    46723749       Europe       0.0030120482
## 20027                  ESP    46723749       Europe       0.0031446541
## 20028                  ESP    46723749       Europe       0.0149700599
## 20029                  ESP    46723749       Europe       0.0025380711
## 20030                  ESP    46723749       Europe       0.0000000000
## 20031                  ESP    46723749       Europe       0.0000000000
## 20032                  ESP    46723749       Europe       0.0099502488
## 20033                  ESP    46723749       Europe       0.0060240964
## 20034                  ESP    46723749       Europe       0.0030395137
## 20035                  ESP    46723749       Europe       0.0008795075
## 20036                  ESP    46723749       Europe       0.0019607843
## 20037                  ESP    46723749       Europe       0.3294528522
## 20038                  ESP    46723749       Europe                NaN
## 20039                  ESP    46723749       Europe       0.1535269710
## 20040                  ESP    46723749       Europe       0.1072961373
## 20041                  ESP    46723749       Europe       0.3850027980
## 20042                  ESP    46723749       Europe       0.1078838174
## 20043                  ESP    46723749       Europe       0.2123552124
## 20044                  ESP    46723749       Europe       0.1600928074
## 20045                  ESP    46723749       Europe       0.2304687500
## 20046                  ESP    46723749       Europe       0.1334355828
## 20047                  ESP    46723749       Europe       0.2019417476
## 20048                  ESP    46723749       Europe       0.2146189736
## 20049                  ESP    46723749       Europe       0.2555948174
## 20050                  ESP    46723749       Europe       0.4191343964
## 20051                  ESP    46723749       Europe       0.3651452282
## 20052                  ESP    46723749       Europe       0.3129770992
## 20053                  ESP    46723749       Europe       0.0418618267
## 20054                  ESP    46723749       Europe       0.3055181696
## 20055                  ESP    46723749       Europe       0.1543052003
## 20056                  ESP    46723749       Europe       0.2312703583
## 20057                  ESP    46723749       Europe       0.2772727273
## 20058                  ESP    46723749       Europe       0.1780558229
## 20059                  ESP    46723749       Europe       0.3519313305
## 20060                  ESP    46723749       Europe       0.1754010695
## 20061                  ESP    46723749       Europe       0.2236628849
## 20062                  ESP    46723749       Europe       0.2025955299
## 20063                  ESP    46723749       Europe       0.2159548751
## 20064                  ESP    46723749       Europe       0.5348288076
## 20065                  ESP    46723749       Europe       0.4144262295
## 20066                  ESP    46723749       Europe       0.0000000000
## 20067                  ESP    46723749       Europe       0.1755027422
## 20068                  ESP    46723749       Europe       0.1508379888
## 20069                  ESP    46723749       Europe       0.1736045412
## 20070                  ESP    46723749       Europe       0.1508916324
## 20071                  ESP    46723749       Europe       0.1881487889
## 20072                  ESP    46723749       Europe       0.1457133175
## 20073                  ESP    46723749       Europe       0.1798917944
## 20074                  ESP    46723749       Europe                Inf
## 20075                  ESP    46723749       Europe       0.1472888425
## 20076                  ESP    46723749       Europe       0.1532215820
## 20077                  ESP    46723749       Europe       0.1615183246
## 20078                  ESP    46723749       Europe       0.1778933092
## 20079                  ESP    46723749       Europe       0.1659178434
## 20080                  ESP    46723749       Europe       0.2042904290
## 20081                  ESP    46723749       Europe       0.1308027699
## 20082                  ESP    46723749       Europe       0.1329670330
## 20083                  ESP    46723749       Europe       0.1505400044
## 20084                  ESP    46723749       Europe       0.1317667537
## 20085                  ESP    46723749       Europe       0.1330825721
## 20086                  ESP    46723749       Europe       0.1222883471
## 20087                  ESP    46723749       Europe       0.1838014726
## 20088                  ESP    46723749       Europe       0.1462136273
## 20089                  ESP    46723749       Europe       0.1398769323
## 20090                  ESP    46723749       Europe       0.1306380638
## 20091                  ESP    46723749       Europe       0.1139390742
## 20092                  ESP    46723749       Europe       0.1145285310
## 20093                  ESP    46723749       Europe       0.0996930632
## 20094                  ESP    46723749       Europe       0.1441596422
## 20095                  ESP    46723749       Europe       0.1295143213
## 20096                  ESP    46723749       Europe       0.0932799612
## 20097                  ESP    46723749       Europe       0.0713429910
## 20098                  ESP    46723749       Europe       0.0824212642
## 20099                  ESP    46723749       Europe       0.0600958728
## 20100                  ESP    46723749       Europe       0.0600702119
## 20101                  ESP    46723749       Europe       0.0848041326
## 20102                  ESP    46723749       Europe       0.0665844636
## 20103                  ESP    46723749       Europe       0.0431509365
## 20104                  ESP    46723749       Europe       0.0369884001
## 20105                  ESP    46723749       Europe       0.0264393378
## 20106                  ESP    46723749       Europe       0.0492690850
## 20107                  ESP    46723749       Europe       0.0083899321
## 20108                  ESP    46723749       Europe       0.0890973036
## 20109                  ESP    46723749       Europe       0.0069962687
## 20110                  ESP    46723749       Europe       0.0219845514
## 20111                  ESP    46723749       Europe       0.0241672110
## 20112                  ESP    46723749       Europe       0.0104076323
## 20113                  ESP    46723749       Europe       0.0071794872
## 20114                  ESP    46723749       Europe       0.0297927461
## 20115                  ESP    46723749       Europe       0.0000000000
## 20116                  ESP    46723749       Europe       0.0000000000
## 20117                  ESP    46723749       Europe       0.0085836910
## 20118                  ESP    46723749       Europe       0.0109289617
## 20119                  ESP    46723749       Europe       0.0123456790
## 20120                  ESP    46723749       Europe       0.0000000000
## 20121                  ESP    46723749       Europe       0.0000000000
## 20122                  ESP    46723749       Europe       0.0000000000
## 20123                  ESP    46723749       Europe       0.0000000000
## 20124                  ESP    46723749       Europe       0.0000000000
## 20125                  ESP    46723749       Europe       0.0000000000
## 20126                  ESP    46723749       Europe       0.0000000000
## 20127                  ESP    46723749       Europe       0.0000000000
## 20128                  ESP    46723749       Europe       0.0000000000
## 20129                  ESP    46723749       Europe                NaN
## 20130                  ESP    46723749       Europe                NaN
## 20131                  ESP    46723749       Europe                NaN
## 20132                  ESP    46723749       Europe                NaN
## 20133                  ESP    46723749       Europe                NaN
## 20134                  ESP    46723749       Europe                NaN
## 20135                  ESP    46723749       Europe                NaN
## 20136                  ESP    46723749       Europe                NaN
## 20137                  ESP    46723749       Europe                NaN
## 20138                  ESP    46723749       Europe                NaN
## 20139                  ESP    46723749       Europe                NaN
## 20140                  ESP    46723749       Europe                NaN
## 20141                  ESP    46723749       Europe                NaN
## 20142                  ESP    46723749       Europe                NaN
## 20143                  ESP    46723749       Europe       0.0000000000
## 20144                  ESP    46723749       Europe                NaN
## 20145                  ESP    46723749       Europe                NaN
## 20146                  ESP    46723749       Europe                NaN
## 20147                  ESP    46723749       Europe                NaN
## 20148                  ESP    46723749       Europe                NaN
## 20149                  ESP    46723749       Europe                NaN
## 20150                  ESP    46723749       Europe                NaN
## 20151                  ESP    46723749       Europe                NaN
## 20152                  ESP    46723749       Europe       0.0000000000
## 20153                  ESP    46723749       Europe                NaN
## 20154                  ESP    46723749       Europe                NaN
## 20155                  ESP    46723749       Europe                NaN
## 20156                  ESP    46723749       Europe                NaN
## 20157                  ESP    46723749       Europe                NaN
## 20158                  ESP    46723749       Europe                NaN
## 20159                  ESP    46723749       Europe                NaN
## 20160                  ESP    46723749       Europe                NaN
## 20161                  ESP    46723749       Europe                NaN
## 20162                  ESP    46723749       Europe                NaN
## 20163                  ESP    46723749       Europe                NaN
## 20164                  ESP    46723749       Europe                NaN
## 20165                  ESP    46723749       Europe                NaN
## 20166                  ESP    46723749       Europe                NaN
## 20167                  ESP    46723749       Europe                NaN
## 20168                  ESP    46723749       Europe                NaN
## 20169                  ESP    46723749       Europe                NaN
## 20170                  ESP    46723749       Europe                NaN
## 20171                  ESP    46723749       Europe                NaN
## 20172                  ESP    46723749       Europe                NaN
## 20173                  ESP    46723749       Europe                NaN
## 20174                  ESP    46723749       Europe                NaN
## 20175                  ESP    46723749       Europe                NaN
## 20176                  ESP    46723749       Europe                NaN
## 20177                  ESP    46723749       Europe                NaN
## 20178                  ESP    46723749       Europe                NaN
## 20179                  ESP    46723749       Europe                NaN
## 20180                  ESP    46723749       Europe                NaN
## 20181                  ESP    46723749       Europe                NaN
## 20182                  ESP    46723749       Europe                NaN
## 20183                  ESP    46723749       Europe                NaN
## 20184                  ESP    46723749       Europe                NaN
## 22195                  GBR    66488991       Europe       0.1270175439
## 22196                  GBR    66488991       Europe       0.1310837119
## 22197                  GBR    66488991       Europe       0.1192733017
## 22198                  GBR    66488991       Europe       0.2442671984
## 22199                  GBR    66488991       Europe       0.1642734061
## 22200                  GBR    66488991       Europe       0.0456431535
## 22201                  GBR    66488991       Europe       0.0580693816
## 22202                  GBR    66488991       Europe       0.1310211946
## 22203                  GBR    66488991       Europe       0.2163636364
## 22204                  GBR    66488991       Europe       0.0975069252
## 22205                  GBR    66488991       Europe       0.1918760021
## 22206                  GBR    66488991       Europe       0.1960072595
## 22207                  GBR    66488991       Europe       0.3541401274
## 22208                  GBR    66488991       Europe       0.0583677686
## 22209                  GBR    66488991       Europe       0.1340399002
## 22210                  GBR    66488991       Europe       0.1546539379
## 22211                  GBR    66488991       Europe       0.1997880233
## 22212                  GBR    66488991       Europe       0.2046696473
## 22213                  GBR    66488991       Europe       0.0331437052
## 22214                  GBR    66488991       Europe       0.0744615385
## 22215                  GBR    66488991       Europe       0.0490644491
## 22216                  GBR    66488991       Europe       0.0953024670
## 22217                  GBR    66488991       Europe       0.1067843018
## 22218                  GBR    66488991       Europe       0.1292543021
## 22219                  GBR    66488991       Europe                Inf
## 22220                  GBR    66488991       Europe       0.2259535655
## 22221                  GBR    66488991       Europe       0.0590188122
## 22222                  GBR    66488991       Europe       0.0481041313
## 22223                  GBR    66488991       Europe       0.1356521739
## 22224                  GBR    66488991       Europe       0.1078651685
## 22225                  GBR    66488991       Europe       0.1242019733
## 22226                  GBR    66488991       Europe       0.1523750771
## 22227                  GBR    66488991       Europe       0.1842491919
## 22228                  GBR    66488991       Europe       0.0541655920
## 22229                  GBR    66488991       Europe       0.0683150650
## 22230                  GBR    66488991       Europe       0.0888090349
## 22231                  GBR    66488991       Europe       0.1346526135
## 22232                  GBR    66488991       Europe       0.0977511788
## 22233                  GBR    66488991       Europe       0.1044920303
## 22234                  GBR    66488991       Europe       0.1572855197
## 22235                  GBR    66488991       Europe       0.0722710163
## 22236                  GBR    66488991       Europe       0.0725973727
## 22237                  GBR    66488991       Europe       0.1292134831
## 22238                  GBR    66488991       Europe       0.1191743267
## 22239                  GBR    66488991       Europe       0.1117374005
## 22240                  GBR    66488991       Europe       0.1950441609
## 22241                  GBR    66488991       Europe       0.2274774775
## 22242                  GBR    66488991       Europe       0.0784404734
## 22243                  GBR    66488991       Europe       0.0941071028
## 22244                  GBR    66488991       Europe       0.1715855893
## 22245                  GBR    66488991       Europe       0.1865948756
## 22246                  GBR    66488991       Europe       0.1586297185
## 22247                  GBR    66488991       Europe       0.1880476297
## 22248                  GBR    66488991       Europe       0.2724947687
## 22249                  GBR    66488991       Europe       0.1195466210
## 22250                  GBR    66488991       Europe       0.0851282051
## 22251                  GBR    66488991       Europe       0.2018099548
## 22252                  GBR    66488991       Europe       0.1669941061
## 22253                  GBR    66488991       Europe       0.2228719948
## 22254                  GBR    66488991       Europe       0.1829241799
## 22255                  GBR    66488991       Europe       0.1987814166
## 22256                  GBR    66488991       Europe       0.1713496085
## 22257                  GBR    66488991       Europe       0.1297276853
## 22258                  GBR    66488991       Europe       0.0962266315
## 22259                  GBR    66488991       Europe       0.2217516843
## 22260                  GBR    66488991       Europe       0.2539134438
## 22261                  GBR    66488991       Europe       0.1883081406
## 22262                  GBR    66488991       Europe       0.2856356632
## 22263                  GBR    66488991       Europe       0.1493950552
## 22264                  GBR    66488991       Europe       0.1090970693
## 22265                  GBR    66488991       Europe       0.2034805890
## 22266                  GBR    66488991       Europe       0.1604494382
## 22267                  GBR    66488991       Europe       0.1536286522
## 22268                  GBR    66488991       Europe       0.1549491212
## 22269                  GBR    66488991       Europe       0.1269524759
## 22270                  GBR    66488991       Europe       0.1428025964
## 22271                  GBR    66488991       Europe       0.0879572544
## 22272                  GBR    66488991       Europe       0.1154752553
## 22273                  GBR    66488991       Europe       0.0984402080
## 22274                  GBR    66488991       Europe       0.0859558478
## 22275                  GBR    66488991       Europe       0.1280991736
## 22276                  GBR    66488991       Europe       0.1044148563
## 22277                  GBR    66488991       Europe       0.0765253361
## 22278                  GBR    66488991       Europe       0.0526315789
## 22279                  GBR    66488991       Europe       0.0541062802
## 22280                  GBR    66488991       Europe       0.0509915014
## 22281                  GBR    66488991       Europe       0.0664605873
## 22282                  GBR    66488991       Europe       0.0500000000
## 22283                  GBR    66488991       Europe       0.0393120393
## 22284                  GBR    66488991       Europe       0.1447368421
## 22285                  GBR    66488991       Europe       0.0597609562
## 22286                  GBR    66488991       Europe       0.0415704388
## 22287                  GBR    66488991       Europe       0.0085470085
## 22288                  GBR    66488991       Europe       0.0149253731
## 22289                  GBR    66488991       Europe       0.0000000000
## 22290                  GBR    66488991       Europe       0.0769230769
## 22291                  GBR    66488991       Europe       0.0208333333
## 22292                  GBR    66488991       Europe       0.0000000000
## 22293                  GBR    66488991       Europe       0.0232558140
## 22294                  GBR    66488991       Europe       0.0208333333
## 22295                  GBR    66488991       Europe       0.0000000000
## 22296                  GBR    66488991       Europe       0.0000000000
## 22297                  GBR    66488991       Europe       0.0000000000
## 22298                  GBR    66488991       Europe       0.0000000000
## 22299                  GBR    66488991       Europe       0.0000000000
## 22300                  GBR    66488991       Europe       0.0000000000
## 22301                  GBR    66488991       Europe       0.0000000000
## 22302                  GBR    66488991       Europe       0.0000000000
## 22303                  GBR    66488991       Europe                NaN
## 22304                  GBR    66488991       Europe                NaN
## 22305                  GBR    66488991       Europe                NaN
## 22306                  GBR    66488991       Europe       0.0000000000
## 22307                  GBR    66488991       Europe                NaN
## 22308                  GBR    66488991       Europe                NaN
## 22309                  GBR    66488991       Europe                NaN
## 22310                  GBR    66488991       Europe                NaN
## 22311                  GBR    66488991       Europe                NaN
## 22312                  GBR    66488991       Europe                NaN
## 22313                  GBR    66488991       Europe                NaN
## 22314                  GBR    66488991       Europe                NaN
## 22315                  GBR    66488991       Europe                NaN
## 22316                  GBR    66488991       Europe                NaN
## 22317                  GBR    66488991       Europe       0.0000000000
## 22318                  GBR    66488991       Europe                NaN
## 22319                  GBR    66488991       Europe       0.0000000000
## 22320                  GBR    66488991       Europe                NaN
## 22321                  GBR    66488991       Europe       0.0000000000
## 22322                  GBR    66488991       Europe                NaN
## 22323                  GBR    66488991       Europe       0.0000000000
## 22324                  GBR    66488991       Europe                NaN
## 22325                  GBR    66488991       Europe                NaN
## 22326                  GBR    66488991       Europe                NaN
## 22327                  GBR    66488991       Europe                NaN
## 22328                  GBR    66488991       Europe                NaN
## 22329                  GBR    66488991       Europe                NaN
## 22330                  GBR    66488991       Europe       0.0000000000
## 22331                  GBR    66488991       Europe                NaN
## 22332                  GBR    66488991       Europe                NaN
## 22333                  GBR    66488991       Europe                NaN
## 22334                  GBR    66488991       Europe                NaN
## 22335                  GBR    66488991       Europe                NaN
## 22336                  GBR    66488991       Europe                NaN
## 22337                  GBR    66488991       Europe                NaN
## 22338                  GBR    66488991       Europe                NaN
## 22339                  GBR    66488991       Europe                NaN
## 22340                  GBR    66488991       Europe                NaN
## 22341                  GBR    66488991       Europe                NaN
## 22342                  GBR    66488991       Europe                NaN
## 22343                  GBR    66488991       Europe                NaN
## 22344                  GBR    66488991       Europe                NaN
## 22345                  GBR    66488991       Europe                NaN
## 22346                  GBR    66488991       Europe                NaN
## 22347                  GBR    66488991       Europe                NaN
## 22348                  GBR    66488991       Europe                NaN
## 22349                  GBR    66488991       Europe                NaN
## 22350                  GBR    66488991       Europe                NaN
## 22351                  GBR    66488991       Europe                NaN
## 22352                  GBR    66488991       Europe                NaN
## 22353                  GBR    66488991       Europe                NaN
## 22354                  GBR    66488991       Europe                NaN
## 22355                  GBR    66488991       Europe                NaN
## 22356                  GBR    66488991       Europe                NaN
## 22357                  GBR    66488991       Europe                NaN
## 22358                  GBR    66488991       Europe                NaN
## 22359                  GBR    66488991       Europe                NaN
## 22360                  GBR    66488991       Europe                NaN
## 22361                  GBR    66488991       Europe                NaN
## 22452                  USA   327167434      America       0.0300313234
## 22453                  USA   327167434      America       0.0331136160
## 22454                  USA   327167434      America       0.0391557051
## 22455                  USA   327167434      America       0.0445328418
## 22456                  USA   327167434      America       0.0535226359
## 22457                  USA   327167434      America       0.0261927532
## 22458                  USA   327167434      America       0.0319253879
## 22459                  USA   327167434      America       0.0296539621
## 22460                  USA   327167434      America       0.0370164429
## 22461                  USA   327167434      America       0.0490066225
## 22462                  USA   327167434      America       0.0504594142
## 22463                  USA   327167434      America       0.0503309969
## 22464                  USA   327167434      America       0.0362325714
## 22465                  USA   327167434      America       0.0303932953
## 22466                  USA   327167434      America       0.0405631626
## 22467                  USA   327167434      America       0.0481114576
## 22468                  USA   327167434      America       0.0538570839
## 22469                  USA   327167434      America       0.0815127397
## 22470                  USA   327167434      America       0.0368059228
## 22471                  USA   327167434      America       0.0262274444
## 22472                  USA   327167434      America       0.0307759627
## 22473                  USA   327167434      America       0.0508570352
## 22474                  USA   327167434      America       0.0540439806
## 22475                  USA   327167434      America       0.0496579382
## 22476                  USA   327167434      America       0.0651921838
## 22477                  USA   327167434      America       0.0785177767
## 22478                  USA   327167434      America       0.0362162905
## 22479                  USA   327167434      America       0.0428124834
## 22480                  USA   327167434      America       0.0484338629
## 22481                  USA   327167434      America       0.0651560295
## 22482                  USA   327167434      America       0.0653207088
## 22483                  USA   327167434      America       0.0840150130
## 22484                  USA   327167434      America       0.0772405660
## 22485                  USA   327167434      America       0.0638074736
## 22486                  USA   327167434      America       0.0362325995
## 22487                  USA   327167434      America       0.0630173356
## 22488                  USA   327167434      America       0.0560151352
## 22489                  USA   327167434      America       0.0789241778
## 22490                  USA   327167434      America       0.0975215517
## 22491                  USA   327167434      America       0.0899291137
## 22492                  USA   327167434      America       0.0554153941
## 22493                  USA   327167434      America       0.0519381708
## 22494                  USA   327167434      America       0.0449672221
## 22495                  USA   327167434      America       0.0607274334
## 22496                  USA   327167434      America       0.0681886553
## 22497                  USA   327167434      America       0.0955500256
## 22498                  USA   327167434      America       0.0874357699
## 22499                  USA   327167434      America       0.0607337740
## 22500                  USA   327167434      America       0.0628141639
## 22501                  USA   327167434      America       0.0447567434
## 22502                  USA   327167434      America       0.0493630573
## 22503                  USA   327167434      America       0.1197679237
## 22504                  USA   327167434      America       0.0978508074
## 22505                  USA   327167434      America       0.0676875218
## 22506                  USA   327167434      America       0.0661678247
## 22507                  USA   327167434      America       0.0720295923
## 22508                  USA   327167434      America       0.0563756758
## 22509                  USA   327167434      America       0.1222715921
## 22510                  USA   327167434      America       0.0725992358
## 22511                  USA   327167434      America       0.1634602627
## 22512                  USA   327167434      America       0.0894435777
## 22513                  USA   327167434      America       0.0615833433
## 22514                  USA   327167434      America       0.0543084721
## 22515                  USA   327167434      America       0.0644922687
## 22516                  USA   327167434      America       0.0587440538
## 22517                  USA   327167434      America       0.0552491077
## 22518                  USA   327167434      America       0.0576778801
## 22519                  USA   327167434      America       0.0622611309
## 22520                  USA   327167434      America       0.0439121756
## 22521                  USA   327167434      America       0.0451216631
## 22522                  USA   327167434      America       0.0392156863
## 22523                  USA   327167434      America       0.0340478026
## 22524                  USA   327167434      America       0.0317498872
## 22525                  USA   327167434      America       0.0390731653
## 22526                  USA   327167434      America       0.0363629090
## 22527                  USA   327167434      America       0.0306089373
## 22528                  USA   327167434      America       0.0173202614
## 22529                  USA   327167434      America       0.0242254367
## 22530                  USA   327167434      America       0.0219844878
## 22531                  USA   327167434      America       0.0146454724
## 22532                  USA   327167434      America       0.0178328439
## 22533                  USA   327167434      America       0.0240072818
## 22534                  USA   327167434      America       0.0105909576
## 22535                  USA   327167434      America       0.0154864641
## 22536                  USA   327167434      America       0.0112312228
## 22537                  USA   327167434      America       0.0204689245
## 22538                  USA   327167434      America       0.0000000000
## 22539                  USA   327167434      America       0.0140562249
## 22540                  USA   327167434      America       0.0130237826
## 22541                  USA   327167434      America       0.0180383315
## 22542                  USA   327167434      America       0.0145808019
## 22543                  USA   327167434      America       0.0128700129
## 22544                  USA   327167434      America       0.0136986301
## 22545                  USA   327167434      America       0.0284900285
## 22546                  USA   327167434      America       0.0069686411
## 22547                  USA   327167434      America       0.0073800738
## 22548                  USA   327167434      America       0.0250000000
## 22549                  USA   327167434      America       0.0330578512
## 22550                  USA   327167434      America       0.0315789474
## 22551                  USA   327167434      America       0.0190476190
## 22552                  USA   327167434      America       0.0135135135
## 22553                  USA   327167434      America       0.0588235294
## 22554                  USA   327167434      America       0.1363636364
## 22555                  USA   327167434      America       0.2857142857
## 22556                  USA   327167434      America       0.0500000000
## 22557                  USA   327167434      America       0.3333333333
## 22558                  USA   327167434      America       0.0000000000
## 22559                  USA   327167434      America       0.0000000000
## 22560                  USA   327167434      America       0.0000000000
## 22561                  USA   327167434      America                NaN
## 22562                  USA   327167434      America       0.0000000000
## 22563                  USA   327167434      America                NaN
## 22564                  USA   327167434      America                NaN
## 22565                  USA   327167434      America       0.0000000000
## 22566                  USA   327167434      America       0.0000000000
## 22567                  USA   327167434      America                NaN
## 22568                  USA   327167434      America                NaN
## 22569                  USA   327167434      America                NaN
## 22570                  USA   327167434      America                NaN
## 22571                  USA   327167434      America                NaN
## 22572                  USA   327167434      America                NaN
## 22573                  USA   327167434      America       0.0000000000
## 22574                  USA   327167434      America       0.0000000000
## 22575                  USA   327167434      America                NaN
## 22576                  USA   327167434      America       0.0000000000
## 22577                  USA   327167434      America                NaN
## 22578                  USA   327167434      America                NaN
## 22579                  USA   327167434      America                NaN
## 22580                  USA   327167434      America                NaN
## 22581                  USA   327167434      America       0.0000000000
## 22582                  USA   327167434      America                NaN
## 22583                  USA   327167434      America                NaN
## 22584                  USA   327167434      America       0.0000000000
## 22585                  USA   327167434      America       0.0000000000
## 22586                  USA   327167434      America       0.0000000000
## 22587                  USA   327167434      America       0.0000000000
## 22588                  USA   327167434      America                NaN
## 22589                  USA   327167434      America                NaN
## 22590                  USA   327167434      America                NaN
## 22591                  USA   327167434      America       0.0000000000
## 22592                  USA   327167434      America                NaN
## 22593                  USA   327167434      America       0.0000000000
## 22594                  USA   327167434      America                NaN
## 22595                  USA   327167434      America                NaN
## 22596                  USA   327167434      America                NaN
## 22597                  USA   327167434      America       0.0000000000
## 22598                  USA   327167434      America                NaN
## 22599                  USA   327167434      America                NaN
## 22600                  USA   327167434      America                NaN
## 22601                  USA   327167434      America                NaN
## 22602                  USA   327167434      America                NaN
## 22603                  USA   327167434      America                NaN
## 22604                  USA   327167434      America                NaN
## 22605                  USA   327167434      America                NaN
## 22606                  USA   327167434      America                NaN
## 22607                  USA   327167434      America                NaN
## 22608                  USA   327167434      America                NaN
## 22609                  USA   327167434      America                NaN
## 22610                  USA   327167434      America                NaN
## 22611                  USA   327167434      America                NaN
## 22612                  USA   327167434      America                NaN
## 22613                  USA   327167434      America                NaN
## 22614                  USA   327167434      America                NaN
## 22615                  USA   327167434      America                NaN
## 22616                  USA   327167434      America                NaN
## 22617                  USA   327167434      America                NaN
## 22618                  USA   327167434      America                NaN
topTenCountries <- topTenCountries[order(format(as.Date(topTenCountries$dateRep, '%d/%m/%Y'), '%m/%d/%Y')),]

view(topTenCountries)
# to find out 'first case registered' for each country 

newtenSub <- topTenCountries %>% 
  group_by(countryterritoryCode, dateRep,  cases) %>% 
  filter(cases == 1 || cases==2 || cases==3)

unique(newtenSub)
## # A tibble: 56 x 11
## # Groups:   countryterritoryCode, dateRep, cases [56]
##    dateRep day   month year  cases deaths countriesAndTer… countryterritor…
##    <fct>   <fct> <fct> <fct> <dbl>  <dbl> <chr>            <chr>           
##  1 21/01/… 21    1     2020      1      0 United_States_o… USA             
##  2 25/01/… 25    1     2020      1      0 United_States_o… USA             
##  3 27/01/… 27    1     2020      3      0 United_States_o… USA             
##  4 28/01/… 28    1     2020      1      0 Germany          DEU             
##  5 29/01/… 29    1     2020      3      0 Germany          DEU             
##  6 30/01/… 30    1     2020      1      0 India            IND             
##  7 31/01/… 31    1     2020      1      0 Germany          DEU             
##  8 31/01/… 31    1     2020      3      0 Italy            ITA             
##  9 31/01/… 31    1     2020      2      0 United_Kingdom   GBR             
## 10 31/01/… 31    1     2020      1      0 United_States_o… USA             
## # … with 46 more rows, and 3 more variables: popData2018 <dbl>,
## #   continentExp <fct>, case_to_fatalities <dbl>
newtenSub[!duplicated(newtenSub$countryterritoryCode),]
## # A tibble: 10 x 11
## # Groups:   countryterritoryCode, dateRep, cases [10]
##    dateRep day   month year  cases deaths countriesAndTer… countryterritor…
##    <fct>   <fct> <fct> <fct> <dbl>  <dbl> <chr>            <chr>           
##  1 21/01/… 21    1     2020      1      0 United_States_o… USA             
##  2 28/01/… 28    1     2020      1      0 Germany          DEU             
##  3 30/01/… 30    1     2020      1      0 India            IND             
##  4 31/01/… 31    1     2020      3      0 Italy            ITA             
##  5 31/01/… 31    1     2020      2      0 United_Kingdom   GBR             
##  6 01/02/… 1     2     2020      2      0 Russia           RUS             
##  7 01/02/… 1     2     2020      1      0 Spain            ESP             
##  8 20/02/… 20    2     2020      2      2 Iran             IRN             
##  9 26/02/… 26    2     2020      1      0 Brazil           BRA             
## 10 07/03/… 7     3     2020      1      0 Peru             PER             
## # … with 3 more variables: popData2018 <dbl>, continentExp <fct>,
## #   case_to_fatalities <dbl>
# Above is the list of all ten top countries and their first date of case reporting/registeration.
cumulativeCovid5 <- tibble(
  covid_entire %>% 
    group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
    mutate("cumulative_cases" = cumsum(cases), 
           "cumulative_deaths" = cumsum(deaths), 
           "case_to_fatalities" = (cumulative_deaths / cumulative_cases)*100) %>%
    select(dateRep, countriesAndTerritories, cases, cumulative_cases, 
           deaths, cumulative_deaths, case_to_fatalities)
)

cumulativeCovid5
## # A tibble: 23,428 x 7
##    dateRep countriesAndTer… cases cumulative_cases deaths cumulative_deat…
##    <chr>   <chr>            <dbl>            <dbl>  <dbl>            <dbl>
##  1 01/01/… Afghanistan          0                0      0                0
##  2 01/02/… Afghanistan          0                0      0                0
##  3 01/03/… Afghanistan          0                0      0                0
##  4 01/04/… Afghanistan          0                0      0                0
##  5 01/05/… Afghanistan          0                0      0                0
##  6 01/06/… Afghanistan          0                0      0                0
##  7 01/07/… Afghanistan          0                0      0                0
##  8 01/08/… Afghanistan          0                0      0                0
##  9 01/09/… Afghanistan          0                0      0                0
## 10 01/10/… Afghanistan          0                0      0                0
## # … with 23,418 more rows, and 1 more variable: case_to_fatalities <dbl>
view(cumulativeCovid5)
cumulativeCovid <- tibble(
  covid_entire %>% 
    group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
    # since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
    # using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
    mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
    # using mutate() function to create two new variables
    select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
    # selecting required variables to view and analyse
## Graph of topTen countries: progress of cases till today: 

today_total <- cumulativeCovid %>% group_by(countriesAndTerritories) %>% top_n(1, dateRep) %>% pull(cumulative_cases)

#cumulativeCovid %>% filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>% 
#  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, color = countriesAndTerritories, group = countriesAndTerritories)) + 
#  geom_line(stat = "identity") + geom_text(stat = 'count', aes(label = today_total))
  #scale_y_continuous(sec.axis = sec_axis(~ ., breaks = today_total))


cumulativeCovid %>% 
  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  annotate(geom = 'text', x = 01/21/2020, y = 1, label = "..USA..", size = 4) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "Top Ten Countries: COVID-19", 
       subtitle = "Highest Number of Cases")

## How to label total today's cumulative total inside the graph?
## How to separate numbers that are getting cramped?
## How to label the date - when first case registered?
# Case_to_fatalities graph

d_ends <- cumulativeCovid5 %>%  group_by(countriesAndTerritories) %>% top_n(1, dateRep) %>%    pull(case_to_fatalities)

#cumulativeCovid5 %>% 
#  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
#  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
#                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
#  geom_line(stat = "identity") + scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends)) + 
#  facet_wrap(~ countriesAndTerritories)


cumulativeCovid5 %>% 
  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Top 10 Countries Death to Case Ratio") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
## Warning: Removed 304 row(s) containing missing values (geom_path).

** ANALYSIS: USA being the front runner both in terms of number of cases and number of deaths. Another country from America continent (Brazil) has reached almost 0.9 million. European countries dominating the over all numbers (total 5 countries) in the top 10 list. Number of cases in India are growing rapidly in within short time span reached number 4 position.

========================== x ======================================

Question 6)

Use the mutate verb in dplyr to calculate the cumulative cases and deaths for each country. The new fields should be named cumulative_cases and cumulative_deaths respectively.

Answer 6)

#cumulativeCovid1 <- tibble(
#  covid_entire %>%
#    group_by(countriesAndTerritories, dateRep) %>%
#    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
#    mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
#    select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths)
#)

#cumulativeCovid1

#view(cumulativeCovid1)

## wrong way to have datewise cumualtive
# Right format to have datewise cumualtive 

cumulativeCovid <- tibble(
  covid_entire %>% 
    group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
    # since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
    # using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
    mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
    # using mutate() function to create two new variables
    select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
    # selecting required variables to view and analyse

cumulativeCovid
## # A tibble: 23,428 x 6
##    dateRep   countriesAndTerrito… cases cumulative_cases deaths cumulative_deat…
##    <chr>     <chr>                <dbl>            <dbl>  <dbl>            <dbl>
##  1 01/01/20… Afghanistan              0                0      0                0
##  2 01/02/20… Afghanistan              0                0      0                0
##  3 01/03/20… Afghanistan              0                0      0                0
##  4 01/04/20… Afghanistan              0                0      0                0
##  5 01/05/20… Afghanistan              0                0      0                0
##  6 01/06/20… Afghanistan              0                0      0                0
##  7 01/07/20… Afghanistan              0                0      0                0
##  8 01/08/20… Afghanistan              0                0      0                0
##  9 01/09/20… Afghanistan              0                0      0                0
## 10 01/10/20… Afghanistan              0                0      0                0
## # … with 23,418 more rows

========================== x ======================================

Question 7)

Create a function called, casesByCountry(), that takes a user defined date and country code as its arguments and displays the distribution of cases for the selected country, leading up to the chosen date. Annotate the chart to show the highest number of cases that were reported; this should correspond with the daily reported case and not the cumulative cases. The chart should also contain a subtitle that indicates the population for the selected country. Note: if a date is not specified, make the current date the default. If a country is not specified, display a message to the user.

Answer 7)

1. user defined date()
2. user defined country code
3. distribution of cases in selected country
4. to date() ... can be today() ... going inlines with thought process in Q.3
5. Annotate the chart to show the highest number of cases that were reported; this should correspond with the daily reported case and not the cumulative cases: ?how to
6. The chart should also contain a subtitle that indicates the population for the selected country: labs() function
7. if a date is not specified, make the current date the default: else() argument 
8. If a country is not specified, display a message to the user: else() argument
caseByCountry <- function(ct, dt = format(today(), '%m/%d/%Y')) {
  ct_len <- nchar(ct)
  #print(x_len)
  if(ct_len!='' & ct_len==3){
    #print(class(x_len))
    country_code <-  covid_entire[covid_entire$countryterritoryCode == ct,]
 } else { 
   return("Kindly enter Country Code")
 }
  date_parameter <- country_code[format(as.Date(country_code$dateRep, '%d/%m/%Y'), '%m/%d/%Y') <= dt,]
  
  pop_18 <- date_parameter$popData2018
  tot_cases <- sum(date_parameter$cases)
  tot_deaths <- sum(date_parameter$deaths)
  
  value <- max(date_parameter$cases)
  if (value < 100){
  y_value <- value + 5
  }
  else if(value<1000){
    y_value <- value + 5
  }
  else if(value<50){
    y_value <- value 
  }
  else if(value >= 30000){
    y_value <- value + 1000
  }
  else{
    y_value <- value + 100
  }
  new <- date_parameter[date_parameter$cases == value,]
  
  x_value <- format(as.Date(new$dateRep, '%d/%m/%Y'), '%m/%d')
  #print(x_value)
  
  #cumulative_deaths <- cumsum(date_parameter$deaths)
  
  country_graph <- date_parameter %>% 
    ggplot(mapping = aes(x = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d'), y = cases)) +   
    geom_bar(stat = "identity", color = "black", fill = "grey", width=1, position=position_dodge())  + 
    geom_bar(data = subset(date_parameter,cases==max(cases)),stat = "identity",colour = "black", fill = "blue", width = 1, position = position_dodge()) +
    #geom_line(aes(y = cumulative_deaths), stat = "identity", color = "Orange") +
    theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
    annotate(geom = 'text', x = x_value, dt, y=y_value, label=value, size = 3) +
    labs(x = "Timeline", y = "Per-day Cases", title = paste(ct, ": COVID-19 Status as of", dt), 
         subtitle = paste("Population:", pop_18, "|", "Total Cases:", tot_cases, "|", "Total Deaths:", tot_deaths ))
    #print(date_parameter)
    print(country_graph)
}

caseByCountry("USA", "06/10/2020")
## Warning: Ignoring unknown aesthetics: xmin

# Kindly enter 3-digit Country Code
# Kindly enter date in the format of '%m/%d/%Y'

========================== x ======================================

Questions 8)

Select a country, of your choice, and use the casesByCountry() function to show the progression of the total COVID-19 cases to-date. Analyze the chart and the supporting data; indicate the total number of cases that were reported and the date of the first reported case. What is the current trend? • Based on your analysis for this country, what are the potential impact on countries like Andorra and San Marino. Create visualizations to support your analysis and/or add other supporting dataset(s) if necessary.

Answer 8)

  1. show the progression of the total COVID-19 cases to-date
  2. Analyze the chart and the supporting data
  3. indicate the total number of cases that were reported
  4. indicate the date of the first reported case
  5. the current trend?
  6. what are the potential impact on countries like Andorra and San Marino
  7. Create visualizations to support your analysis and/or add other supporting dataset(s) if necessary.

-> Examine the progression of the virus in a country of your preference. Then look at Andorra and San Marino and pay close attention to the information that is provided to you about these two countries/territories. Then explain the effect that the trends you observed in your preferred country, could have on Andorra and San Marino (should similar patterns emerge).

-> It may also be helpful to examine the continent that the two countries/territories are located, and if you examine the progression of the virus in a neighboring country, it may become clear why the potential impact is of importance.

# Country selected for Analysis: ITALY

newtenSub <- topTenCountries %>% 
  group_by(countryterritoryCode, dateRep,  cases) %>% 
  filter(cases == 1 || cases==2 || cases==3)

unique(newtenSub)
## # A tibble: 56 x 11
## # Groups:   countryterritoryCode, dateRep, cases [56]
##    dateRep day   month year  cases deaths countriesAndTer… countryterritor…
##    <fct>   <fct> <fct> <fct> <dbl>  <dbl> <chr>            <chr>           
##  1 21/01/… 21    1     2020      1      0 United_States_o… USA             
##  2 25/01/… 25    1     2020      1      0 United_States_o… USA             
##  3 27/01/… 27    1     2020      3      0 United_States_o… USA             
##  4 28/01/… 28    1     2020      1      0 Germany          DEU             
##  5 29/01/… 29    1     2020      3      0 Germany          DEU             
##  6 30/01/… 30    1     2020      1      0 India            IND             
##  7 31/01/… 31    1     2020      1      0 Germany          DEU             
##  8 31/01/… 31    1     2020      3      0 Italy            ITA             
##  9 31/01/… 31    1     2020      2      0 United_Kingdom   GBR             
## 10 31/01/… 31    1     2020      1      0 United_States_o… USA             
## # … with 46 more rows, and 3 more variables: popData2018 <dbl>,
## #   continentExp <fct>, case_to_fatalities <dbl>
newtenSub[!duplicated(newtenSub$countryterritoryCode),]
## # A tibble: 10 x 11
## # Groups:   countryterritoryCode, dateRep, cases [10]
##    dateRep day   month year  cases deaths countriesAndTer… countryterritor…
##    <fct>   <fct> <fct> <fct> <dbl>  <dbl> <chr>            <chr>           
##  1 21/01/… 21    1     2020      1      0 United_States_o… USA             
##  2 28/01/… 28    1     2020      1      0 Germany          DEU             
##  3 30/01/… 30    1     2020      1      0 India            IND             
##  4 31/01/… 31    1     2020      3      0 Italy            ITA             
##  5 31/01/… 31    1     2020      2      0 United_Kingdom   GBR             
##  6 01/02/… 1     2     2020      2      0 Russia           RUS             
##  7 01/02/… 1     2     2020      1      0 Spain            ESP             
##  8 20/02/… 20    2     2020      2      2 Iran             IRN             
##  9 26/02/… 26    2     2020      1      0 Brazil           BRA             
## 10 07/03/… 7     3     2020      1      0 Peru             PER             
## # … with 3 more variables: popData2018 <dbl>, continentExp <fct>,
## #   case_to_fatalities <dbl>
# First day reported in Italy: January 31, 2020
caseByCountry("ITA") 
## Warning: Ignoring unknown aesthetics: xmin

** ANALYSIS: ITALY:

Progression of cases: Within 3 weeks of first reported case, the number of cases started rising. In the second week of March, the number of cases started increasing ‘exponentially’ displaying steep rise in the graph. Reaching it’s peak on March 22 with 6,557 cases in a single day. High number of cases (4,000 and above) remained for four weeks continuous and then showed ‘gradual’ decline. Italy considered as one of the top-most healthcare system couldn’t sustain this high influx of cases resulted into complete break-down of the healthcare system.

The disease took 7 weeks to reach to the peak, remained in peak phase for four weeks, and now almost since 9 weeks it is in declining phase. The country is still registering new cases daily ranging between 150-400.

# Italy: Progression of cases compared with topTen countries

cumulativeCovid %>% 
  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  #annotate(geom = 'text', x = 01/21/2020, y = 1, label = "..USA..", size = 4) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "Top Ten Countries: COVID-19", 
       subtitle = "Highest Number of Cases")

# Italy with total 236,305 cases as of 06/14/2020 is 7th highest contributor in world and 4th highest contributor in Europe continent of COVID-19 cases. 
# Italy: Deaths compared with topTen countries


cumulativeCovid5 %>% 
  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_deaths, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  labs(x = "Timeline", y = "Total Deaths", title = "Top 10 Countries Deaths") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))

# Italy with total 34,223 as of 06/14/2020 is 4th highest contributor in world and 2nd highest contributor in Europe continent of deaths due to COVID-19. 
# Italy: Death to Case Ratio compared with topTen countries

cumulativeCovid5 %>% 
  filter(countriesAndTerritories %in% topTen$countriesAndTerritories) %>%
  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity") + 
  labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Top 10 Countries Death to Case Ratio") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
## Warning: Removed 304 row(s) containing missing values (geom_path).

# Comparison of Death to Case Ratio with other countries in the world: The disease was more aggressive in Italy compared to other top ten contributor countries. United Kingdom is running almost similar ratio to that of Italy (14 deaths/per 100 cases).  
# Cumulative Cases: of European Countries

cumulativeEurope <- tibble(
  covid_entire %>%
    filter(continentExp == "Europe") %>%
    group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
    # since question is for each country and have to calculate cumualtive cases/deaths per day - grouping both variables
    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
    # using cumsum() to calculate cumulative numbers each day for both 'cases' and 'deaths'
    mutate("cumulative_cases" = cumsum(cases), "cumulative_deaths" = cumsum(deaths)) %>%
    # using mutate() function to create two new variables
    select(dateRep, countriesAndTerritories, cases, cumulative_cases, deaths, cumulative_deaths))
    # selecting required variables to view and analyse

cumulativeEurope
## # A tibble: 7,198 x 6
##    dateRep   countriesAndTerrito… cases cumulative_cases deaths cumulative_deat…
##    <chr>     <chr>                <dbl>            <dbl>  <dbl>            <dbl>
##  1 03/09/20… Albania                  2                2      0                0
##  2 03/10/20… Albania                  4                6      0                0
##  3 03/11/20… Albania                  4               10      0                0
##  4 03/12/20… Albania                  1               11      1                1
##  5 03/13/20… Albania                 12               23      0                1
##  6 03/14/20… Albania                 10               33      0                1
##  7 03/15/20… Albania                  5               38      0                1
##  8 03/16/20… Albania                  4               42      0                1
##  9 03/17/20… Albania                  9               51      0                1
## 10 03/18/20… Albania                  4               55      0                1
## # … with 7,188 more rows
# Progression of Cases: Graph: Europe

cumulativeEurope %>% 
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "European Countries: COVID-19", 
       subtitle = "Progression of Cases")

# Comparison of Italy with other European Countries:
# Ten European Countries: Comparison graph: Progression of cases 

cumulativeEurope %>% 
  filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "Ten European Countries: COVID-19", 
       subtitle = "Progression of Cases")

# Continent Europe: COVID-19 Status

covidEurope <- tibble(
  covid_entire %>%
    filter(continentExp == "Europe") %>%
    group_by(countriesAndTerritories, "dateRep" = format(as.Date(dateRep, '%d/%m/%Y'), '%m/%d/%Y')) %>%
    summarise(cases = cumsum(cases), deaths = cumsum(deaths)) %>%
    mutate("cumulative_cases" = cumsum(cases), 
           "cumulative_deaths" = cumsum(deaths), 
           "case_to_fatalities" = (cumulative_deaths / cumulative_cases)*100) %>%
    select(dateRep, countriesAndTerritories, cases, cumulative_cases, 
           deaths, cumulative_deaths, case_to_fatalities))

covidEurope
## # A tibble: 7,198 x 7
##    dateRep countriesAndTer… cases cumulative_cases deaths cumulative_deat…
##    <chr>   <chr>            <dbl>            <dbl>  <dbl>            <dbl>
##  1 03/09/… Albania              2                2      0                0
##  2 03/10/… Albania              4                6      0                0
##  3 03/11/… Albania              4               10      0                0
##  4 03/12/… Albania              1               11      1                1
##  5 03/13/… Albania             12               23      0                1
##  6 03/14/… Albania             10               33      0                1
##  7 03/15/… Albania              5               38      0                1
##  8 03/16/… Albania              4               42      0                1
##  9 03/17/… Albania              9               51      0                1
## 10 03/18/… Albania              4               55      0                1
## # … with 7,188 more rows, and 1 more variable: case_to_fatalities <dbl>
# Progression of Cases: Graph: Europe

cumulativeEurope %>% 
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "European Countries: COVID-19", 
       subtitle = "Progression of Cases")

# Comparison of Italy with other European Countries:
# Progression of Cases: Graph: Ten European Countries

covidEurope %>% 
  filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_cases, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  labs(x = "Timeline", y = "Total Number of Cases", title = "Ten European Countries: COVID-19", 
       subtitle = "Progression of Cases")

# Deaths: Graph: Ten European Countries

covidEurope %>% 
  filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
  ggplot(mapping = aes(x = dateRep, y = cumulative_deaths, 
                       color = countriesAndTerritories, group = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  theme(plot.margin = unit(c(1,3,1,1), "lines")) + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3)) +
  labs(x = "Timeline", y = "Total Number of Deaths", title = "Ten European Countries: Deaths: COVID-19")

# Italy: second hihgest contributor in Europe after United Kingdom. 
# # Death to Case Ratio: Graph: Ten European Countries

covidEurope %>% 
  filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "Ten European Countries: COVID-19", subtitle = "Death to Case Ratio") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
## Warning: Removed 346 row(s) containing missing values (geom_path).

# Death to Case Ratio: of Italy remained second highest in the Europe continent with 14 deaths/per 100 cases registered. This high ratio also indicates vulnerability of the population which is aged. 
# Mortality Rate: Ten European Countries:

europeMortality <- tibble(
  covid_entire %>% filter(countriesAndTerritories %in% c("United_Kingdom", "Italy", "Spain", "France", "Germany", "Andorra", "San_Marino", "Switzerland", "Monaco", "Russia")) %>%
    group_by(dateRep, countriesAndTerritories) %>% 
    summarise(cases = sum(cases), deaths = sum(deaths), 
              population = sum(popData2018)) %>%
    mutate("mortalityRate" = ((deaths/population) * 100000)) %>% # 'denominator' for Mortality rate specific to any disease 
    select(dateRep, countriesAndTerritories, cases, deaths, population, mortalityRate))
  
europeMortality
## # A tibble: 1,582 x 6
##    dateRep    countriesAndTerritories cases deaths population mortalityRate
##    <fct>      <chr>                   <dbl>  <dbl>      <dbl>         <dbl>
##  1 01/01/2020 France                      0      0   66987244             0
##  2 01/01/2020 Germany                     0      0   82927922             0
##  3 01/01/2020 Italy                       0      0   60431283             0
##  4 01/01/2020 Monaco                      0      0      38682             0
##  5 01/01/2020 Russia                      0      0  144478050             0
##  6 01/01/2020 San_Marino                  0      0      33785             0
##  7 01/01/2020 Spain                       0      0   46723749             0
##  8 01/01/2020 Switzerland                 0      0    8516543             0
##  9 01/01/2020 United_Kingdom              0      0   66488991             0
## 10 01/02/2020 France                      0      0   66987244             0
## # … with 1,572 more rows
# Mortality Rate: Graph: Ten European Countries:

europeMortality %>%
  ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) + 
  geom_point() + geom_smooth(aes(color = mortalityRate)) + facet_wrap(~ countriesAndTerritories) +
  #ylim(0, 10) +
  labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population", 
       title = "Progression of Cases & Mortality Rate: Europe")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at -0.045
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.002025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at -0.045
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.045
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger
## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)

===================== x ===========================

ANDORRA:

Andorra is the sixth-smallest nation in Europe, having an area of 468 square kilometres (181 sq mi) and a population of approximately 77,006. The Andorran people are a Romance ethnic group of originally Catalan descent. Andorra is the 16th-smallest country in the world by land and the 11th-smallest by population. Andorra is a tiny country in south western Europe, located in the eastern Pyrenees mountains and bordered by “Spain” and “France”.

** Andorra had the highest life expectancy in the world at 81 years, according to the Global Burden of Disease Study

caseByCountry("AND")
## Warning: Ignoring unknown aesthetics: xmin

** ANALYSIS: ANDORRA:

The Trend: The first case reported on March 3 and reached its peak in 3rd week. the peak phase lasted for about 3 weeks and then cases started declining gradually. May month remianed silent comparatively and there’s a sudden spike of 79 cases (highest one day total) on June 3, 2020. This might be the reason that these cases were diagnosed in May month, but reporting done on June 3?

With 853 cases, we can say that around 1.1% of the population got infected with the disease. This is usually an issue with the country having small population. The imapct is larger comapred to countries with high popualtion. If we consider Mortality rate (which is counted with 100,000 as denominator), even with one death, it will create bigger impact on the ratio. Again, this is evident looking at Mortality rate graph that indicates Andorra mortality of upto 5.5 against neigbouring countries France and Spain (Mortality rate of 2)

With 51 deaths out of 853 cases, Death to Case Ratio of Andorra is around 6, while France (18) and Spain (12) showed much higher death to case ratio.

# Neighbouring Countries: Spain and France

caseByCountry("ESP")
## Warning: Ignoring unknown aesthetics: xmin

caseByCountry("FRA")
## Warning: Ignoring unknown aesthetics: xmin

# Andorra: Mortality Rate in comaprison with neighbouring countries:

  covid_entire %>% filter(countriesAndTerritories %in% c("Spain", "France", "Andorra")) %>%
    group_by(dateRep, countriesAndTerritories) %>% 
    summarise(cases = sum(cases), deaths = sum(deaths), 
              population = sum(popData2018)) %>%
    mutate("mortalityRate" = ((deaths/population) * 100000)) %>% 
  ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) + 
  geom_point(size = 3) +
  #ylim(0, 10) +
  labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population", 
       title = "Progression of Cases & Mortality Rate: Andorra")

# Andorra: Death to Case ratio: in comaprison with neighbouring countries:

covidEurope %>% 
  filter(countriesAndTerritories %in% c("Spain", "France", "Andorra")) %>%
  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "European Country: Andorra", subtitle = "Death to Case Ratio") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
## Warning: Removed 55 row(s) containing missing values (geom_path).

===================== x ===========================

SAN MARINO:

San Marino, officially the Republic of San Marino, also known as the Most Serene Republic of San Marino, is a country in Southern Europe completely enclosed by “Italy”. It is one of only three countries in the world to be completely enclosed by another country. It is the third smallest country in Europe, after Vatican City and Monaco, and the fifth smallest country in the world. San Marino covers a land area of just over 61 km2 (24 sq mi), and has a population of 33,562

caseByCountry("SMR")
## Warning: Ignoring unknown aesthetics: xmin

** ANALYSIS: SAN MARIO: Compare with neighbouring country: Italy

The Trend: The first case reported on February 28 i.e. 4 weeks after the frist case reported in Italy. and reached its peak in 3rd week of March. It was at the same time, where cases in Italy were at peak and registered its highest one-day cases on March 22. The country continues showing high pillars (high number of cases) intermittently unlike the neighbouring country (Italy) that is showing a definite declining trend.

With 703 cases, we can say that around 2.1% of the population got infected with the disease. This is usually an issue with the country having small population that we mentioned in analysis of Andorra. The imapct is larger comapred to countries with high popualtion. Again, this is evident looking at Mortality rate graph that indicates Andorra mortality of upto 17.5 against neigbouring country Italy (Mortality rate of less than 2)

With 36 deaths out of 703 cases, Death to Case Ratio of San Marino is around 6, while Italy (14) showed much higher death to case ratio.

# San Marino: Mortality Rate in comaprison with neighbouring countries:

  covid_entire %>% filter(countriesAndTerritories %in% c("Italy", "San_Marino")) %>%
    group_by(dateRep, countriesAndTerritories) %>% 
    summarise(cases = sum(cases), deaths = sum(deaths), 
              population = sum(popData2018)) %>%
    mutate("mortalityRate" = ((deaths/population) * 100000)) %>% 
  ggplot(mapping = aes(y = mortalityRate, x = cases, color = countriesAndTerritories)) + 
  geom_point(size = 3) +
  #ylim(0, 10) +
  labs(x = "No. of Cases", y = "Mortality Rate / 100,000 Population", 
       title = "Progression of Cases & Mortality Rate: San Marino")

# San Marino: Death to Case ratio: in comaprison with neighbouring countries:

covidEurope %>% 
  filter(countriesAndTerritories %in% c("Italy", "San_Marino")) %>%
  ggplot(mapping = aes(x = dateRep, y = case_to_fatalities, 
                       group = countriesAndTerritories, color = countriesAndTerritories)) + 
  geom_line(stat = "identity", size = 1.2) + 
  labs(x = "Timeline", y = "Death to Case Ratio / per 100", title = "European Country: San Marino", subtitle = "Death to Case Ratio") + 
  theme(axis.text.x = element_text(angle=65, vjust=1,size=3))
## Warning: Removed 88 row(s) containing missing values (geom_path).